Skocz do pozycji: 1.

Tytuł:

K-means is probabilistically poor

Autorzy:

Kłopotek, Mieczysław

Powiązania:

https://bibliotekanauki.pl/articles/2201613.pdf

Data publikacji:

2022

Wydawca:

Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach

Tematy:

k-means
clustering
probabilistic k-richness

Opis:

Kleinberg introduced the concept of k-richness as a requirement for an algorithm to be a clustering algorithm. The most popular algorithm k means dos not fit this definition because of its probabilistic nature. Hence Ackerman et al. proposed the notion of probabilistic k-richness claiming without proof that k-means has this property. It is proven in this paper, by example, that the version of k-means with random initialization does not have the property probabilistic k-richness, just rebuking Ackeman's claim.

Źródło:

Studia Informatica : systems and information technology; 2022, 2(27); 5--26
1731-2264

Pojawia się w:

Studia Informatica : systems and information technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 2.

Tytuł:

Inicjalizacja segmentacji k-means uwzględniająca rozkład gęstości pikseli

Autorzy:

Świta, R.
Suszyński, Z.

Powiązania:

https://bibliotekanauki.pl/articles/118366.pdf

Data publikacji:

2014

Wydawca:

Politechnika Koszalińska. Wydawnictwo Uczelniane

Tematy:

FA
KKZ
k-means
kmeans++
segmentacja
k-means ++
segmentation
High Density

Opis:

Artykuł przedstawia modyfikację inicjalizacji KKZ algorytmu k-means, uwzględniającą, oprócz wzajemnych odległości środków segmentów, również rozkład gęstości pikseli. Funkcja gęstości piksela jest sumą odwrotności odległości piksela od pozostałych i jest poddawana oszacowaniu na podstawie odległości piksela od wartości średniej i wariancji wartości pikseli. W eksperymentach segmentacji podlegały cztery różne sekwencje obrazów termicznych uzyskanych metodą termografii aktywnej. Pomimo dodatkowych obliczeń podczas inicjalizacji, metoda wykazała szybszą zbieżność algorytmu z czasami bardzo podobnymi do inicjalizacji KKZ, ale mniejszym błędem końcowym segmentacji.
This article presents a modification for the KKZ initialization of the k-means segmentation algorithm, which, in addition to the mutual distance of segments, takes into account the density of pixels. Pixel density is expressed asa sum of the inverse of the pixel’s distance to the other pixels and is subjected to estimation based on the distance from the mean and variance of the pixel values. In the experiments, four different sequences of thermal images were used, obtained using active thermography. Despite the additional calculations during initialization, method showed a faster convergence of the algorithm, with processing times very similar to the KKZ initialization, but with a lower final segmentation error.

Źródło:

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej; 2014, 6; 89-98
1897-7421

Pojawia się w:

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Extending k-means with the description comes first approach

Autorzy:

Stefanowski, J.
Weiss, D.

Powiązania:

https://bibliotekanauki.pl/articles/970926.pdf

Data publikacji:

2007

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

document clustering
cluster labels
k-means algorithm
information retrieval

Opis:

This paper describes a technique for clustering large collections of short and medium length text documents such as press articles, news stories and the like. The technique called description comes first (DCF) consists of identification of related document clusters, selection of salient phrases relevant to these clusters and reallocation of documents matching the selected phrases to form final document groups. The advantages of this technique include more comprehensive cluster labels and clearer (more transparent) relationship between cluster labels and their content. We demonstrate the DCF by taking a standard k-means algorithm as a baseline and weaving DCF elements into it; the outcome is the descriptive k-means (DKM) algorithm. The paper goes through technical background explaining how to implement DKM efficiently and ends with the description of an experiment measuring clustering quality on a benchmark document collection 20-newsgroups. Short fragments of this paper appeared at the poster session of the RIAO 2007 conference, Pittsburgh, PA, USA (electronic proceedings only).

Źródło:

Control and Cybernetics; 2007, 36, 4; 1009-1035
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

Tytuł:

K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN

Autorzy:

Angadi, Basavaraj M.
Kakkasageri, Mahabaleshwar S.

Powiązania:

https://bibliotekanauki.pl/articles/27311955.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

wireless sensor networks
cluster
K-Means algorithm
fuzzy logic

Opis:

Wireless Sensor Networks (WSN) acquired a lot of attention due to their widespread use in monitoring hostile environments, critical surveillance and security applications. In these applications, usage of wireless terminals also has grown significantly. Grouping of Sensor Nodes (SN) is called clustering and these sensor nodes are burdened by the exchange of messages caused due to successive and recurring re-clustering, which results in power loss. Since most of the SNs are fitted with nonrechargeable batteries, currently researchers have been concentrating their efforts on enhancing the longevity of these nodes. For battery constrained WSN concerns, the clustering mechanism has emerged as a desirable subject since it is predominantly good at conserving the resources especially energy for network activities. This proposed work addresses the problem of load balancing and Cluster Head (CH) selection in cluster with minimum energy expenditure. So here, we propose hybrid method in which cluster formation is done using unsupervised machine learning based kmeans algorithm and Fuzzy-logic approach for CH selection.

Źródło:

International Journal of Electronics and Telecommunications; 2023, 69, 4; 793--801
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 5.

Tytuł:

DSMK-means “density-based split-and-Merge K-means clustering algorithm

Autorzy:

Aldahdooh, R. T.
Ashour, W.

Powiązania:

https://bibliotekanauki.pl/articles/91719.pdf

Data publikacji:

2013

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

clustering
K-means
Density-based Split
Merge K-means clustering Algorithm
DSMK-means
clustering algorithm

Opis:

Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 51-71
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 6.

Tytuł:

A feasible k-means kernel trick under non-Euclidean feature space

Autorzy:

Kłopotek, Robert
Kłopotek, Mieczysław
Wierzchoń, Sławomir

Powiązania:

https://bibliotekanauki.pl/articles/1838163.pdf

Data publikacji:

2020

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

kernel method
k-means
non-Euclidean feature space
Gower and Legendre theorem

Opis:

This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm. Kernel-k-means is a clustering algorithm, allowing clustering data in a similar way to k-means when an embedding of data points into Euclidean space is not provided and instead a matrix of “distances” (dissimilarities) or similarities is available. The kernel trick allows us to by-pass the need of finding an embedding into Euclidean space. We show that the algorithm returns wrong results if the embedding actually does not exist. This means that the embedding must be found prior to the usage of the algorithm. If it is found, then the kernel trick is pointless. If it is not found, the distance matrix needs to be repaired. But the reparation methods require the construction of an embedding, which first makes the kernel trick pointless, because it is not needed, and second, the kernel-k-means may return different clusterings prior to repairing and after repairing so that the value of the clustering is questioned. In the paper, we identify a distance repairing method that produces the same clustering prior to its application and afterwards and does not need to be performed explicitly, so that the embedding does not need to be constructed explicitly. This renders the kernel trick applicable for kernel-k-means.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2020, 30, 4; 703-715
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

Tytuł:

Kernel K-Means clustering algorithm for identification of glaucoma in ophthalmology

Autorzy:

Stapor, K.
Bruckner, A.

Powiązania:

https://bibliotekanauki.pl/articles/333803.pdf

Data publikacji:

2005

Wydawca:

Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych

Tematy:

grupowanie
segmentacja obrazu
clustering
image segmentation
kernel-based learning

Opis:

This paper presents the improved version of the classification system for supporting glaucoma diagnosis in ophthalmology, proposed in [4]. In this paper we propose the new segmentation step based on the kernel K-Means clustering algorithm which enable for better classification performance.

Źródło:

Journal of Medical Informatics & Technologies; 2005, 9; 167-172
1642-6037

Pojawia się w:

Journal of Medical Informatics & Technologies

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

Segmentacja sekwencji obrazów metodą korelacyjną
Segmentation of the image sequence using the correlation method

Autorzy:

Świta, R.
Suszyński, Z.

Powiązania:

https://bibliotekanauki.pl/articles/152568.pdf

Data publikacji:

2013

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

segmentacja
obrazy termiczne
korelacja
K-means
FCM
segmentation
thermal images
correlation
k-means

Opis:

Artykuł przedstawia nową metodę segmentacji sekwencji obrazów termicznych wyodrębniającą obszary o różnych właściwościach cieplnych. Metoda oparta jest na korelacji położenia i kształtu segmentów w poszczególnych kadrach sekwencji. Segmentacja pozwala zmniejszyć liczbę analizowanych obszarów do kilku tysięcy razy, co stwarza realne możliwości praktycznego wykorzystania tomografii termicznej. Opisana metoda jest porównana z algorytmami klasteryzacji K-Means i FCM. Zaletą algorytmu korelacyjnego jest automatyczne wyznaczanie liczby segmentów wyjściowych.
This paper presents a new method for segmentation of thermal image sequences. Its aim is to divide the sequence into segments with different thermal properties. The described algorithm is based on measurements of the position and shape correlation of the segments in successive frames of the sequence. It is composed of several stages. The first stage consists of segmenting consecutive frames of the sequence (Fig. 2). The second step is analysis of the similarity of each segment in each frame with respect to all other segments of all frames and synthesis of the intermediate segments (Fig. 4). The intermediate segments form the segmented output image using the depth buffer technique to resolve multiple pixel-to-segment assignments (Fig. 6). This method is a basis for the thermal analysis of solids, which results in discovering depth profiles of thermal properties for each area. The segmentation reduces the number of the analyzed areas down to a few thousand times, which creates real opportunities for practical application of thermal tomography. The new algorithm has been compared with the K means algorithm [2], and FCM [6], which minimizes the sum of pixel value deviations from the centers of the segments they are assigned to, for all frames of the sequence (Tab. 1). The advantage of the correlation method is automatic determination of the number of output segments in the image and maintaining the constant segmentation error when increasing the number of the processed frames.

Źródło:

Pomiary Automatyka Kontrola; 2013, R. 59, nr 7, 7; 680-683
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Clustering of data represented by pairwise comparisons

Autorzy:

Dvoenko, Sergey

Powiązania:

https://bibliotekanauki.pl/articles/2183479.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

clustering
k-means
distance
similarity

Opis:

In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.

Źródło:

Control and Cybernetics; 2022, 51, 3; 343--387
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

Tytuł:

An alternative extension of the k-means algorithm for clustering categorical data

Autorzy:

San, O. M.
Huynh, V. N.
Nakamori, Y.

Powiązania:

https://bibliotekanauki.pl/articles/907406.pdf

Data publikacji:

2004

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

analiza skupień
dane kategoryczne
eksploracja danych
cluster analysis
categorical data
data mining

Opis:

Most of the earlier work on clustering has mainly been focused on numerical data whose inherent geometric properties can be exploited to naturally define distance functions between data points. Recently, the problem of clustering categorical data has started drawing interest. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. The main contribution of this paper is to show how to apply the notion of "cluster centers'' on a dataset of categorical objects and how to use this notion for formulating the clustering problem of categorical objects as a partitioning problem. Finally, a k-means-like algorithm for clustering categorical data is introduced. The clustering performance of the algorithm is demonstrated with two well-known data sets, namely, soybean disease and nursery databases.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2004, 14, 2; 241-247
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 11.

Tytuł:

Przegląd technik grupowania danych i obszary zastosowań

Autorzy:

Sala, Karolina

Powiązania:

https://bibliotekanauki.pl/articles/2157869.pdf

Data publikacji:

2017

Wydawca:

Instytut Studiów Międzynarodowych i Edukacji Humanum

Tematy:

cluster analysis
hierarchical clustering
k-means

Opis:

The paper presents an overview of various clustering techniques used in data mining. Clustering is an unsupervised learning problem that is used to identify groups in a set of unlabeled data. Data is grouped by probability so that objects of the same group / cluster have similar properties / characteristics [1]. This article aims at exploring and comparing different clustering algorithms. Grouping is used in many areas, including machine learning, pattern recognition, image analysis, information retrieval.

Źródło:

Społeczeństwo i Edukacja. Międzynarodowe Studia Humanistyczne; 2017, 2(25); 141-145
1898-0171

Pojawia się w:

Społeczeństwo i Edukacja. Międzynarodowe Studia Humanistyczne

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method

Autorzy:

Maździarz, A.

Powiązania:

https://bibliotekanauki.pl/articles/308715.pdf

Data publikacji:

2018

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

alarm correlation
alarm patterns
cluster analysis
mobile telecommunication network
root cause analysis

Opis:

Event correlation and root cause analysis play a fundamental role in the process of troubleshooting all technical faults and malfunctions. An in-depth, complicated multiprotocol analysis can be greatly supported or even replaced by a troubleshooting methodology based on data analysis approaches. The mobile telecommunications domain has been experiencing rapid development recently. Introduction of new technologies and services, as well as multivendor environment distributed across the same geographical area create a lot of challenges in network operation routines. Maintenance tasks have been recently becoming more and more complicated, time consuming and require big data analyses to be performed. Most network maintenance activities are completed manually by experts using raw network management information available in the network management system via multiple applications and direct database queries. With these circumstances considered, identification of network failures is a very difficult, if not an impossible task. This explains why effective yet simple tools and methods providing network operators with carefully selected, essential information are needed. Hence, in this paper efficient approximated alarm correlation algorithm based on the k-means cluster analysis method is proposed.

Źródło:

Journal of Telecommunications and Information Technology; 2018, 2; 95-102
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

Tytuł:

Anomaly detection in a cutting tool by k-means clustering and support vector machines

Autorzy:

Lahrache, A.
Cocconcelli, M.
Rubini, R.

Powiązania:

https://bibliotekanauki.pl/articles/328445.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

knife diagnostics
k-means
hierarchical clustering
support vector machines
diagnostyka
grupowanie hierarchiczne

Opis:

This paper concerns the analysis of experimental data, verifying the applicability of signal analysis techniques for condition monitoring of a packaging machine. In particular, the activity focuses on the cutting process that divides a continuous flow of packaging paper into single packages. The cutting process is made by a steel knife driven by a hydraulic system. Actually, the knives are frequently substituted, causing frequent stops of the machine and consequent lost production costs. The aim of this paper is to develop a diagnostic procedure to assess the wearing condition of blades, reducing the stops for maintenance. The packaging machine was provided with pressure sensor that monitors the hydraulic system driving the blade. Processing the pressure data comprises three main steps: the selection of scalar quantities that could be indicative of the condition of the knife. A clustering analysis was used to set up a threshold between unfaulted and faulted knives. Finally, a Support Vector Machine (SVM) model was applied to classify the technical condition of knife during its lifetime.

Źródło:

Diagnostyka; 2017, 18, 3; 21-29
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

The number of clusters in hybrid predictive models: does it really matter?

Autorzy:

Łapczyński, Mariusz
Jefmański, Bartłomiej

Powiązania:

https://bibliotekanauki.pl/articles/1046637.pdf

Data publikacji:

2020

Wydawca:

Główny Urząd Statystyczny

Tematy:

hybrid predictive model
k-means algorithm
decision trees

Opis:

For quite a long time, research studies have attempted to combine various analytical tools to build predictive models. It is possible to combine tools of the same type (ensemble models, committees) or tools of different types (hybrid models). Hybrid models are used in such areas as customer relationship management (CRM), web usage mining, medical sciences, petroleum geology and anomaly detection in computer networks. Our hybrid model was created as a sequential combination of a cluster analysis and decision trees. In the first step of the procedure, objects were grouped into clusters using the k-means algorithm. The second step involved building a decision tree model with a new independent variable that indicated which cluster the objects belonged to. The analysis was based on 14 data sets collected from publicly accessible repositories. The performance of the models was assessed with the use of measures derived from the confusion matrix, including the accuracy, precision, recall, F-measure, and the lift in the first and second decile. We tried to find a relationship between the number of clusters and the quality of hybrid predictive models. According to our knowledge, similar studies have not been conducted yet. Our research demonstrates that in some cases building hybrid models can improve the performance of predictive models. It turned out that the models with the highest performance measures require building a relatively large number of clusters (from 9 to 15).

Źródło:

Przegląd Statystyczny; 2019, 66, 3; 228-238
0033-2372

Pojawia się w:

Przegląd Statystyczny

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

Tytuł:

Initial Results of Nonhierarchical Cluster Methods Use for Low Flow Grouping

Autorzy:

Cupak, A.

Powiązania:

https://bibliotekanauki.pl/articles/123583.pdf

Data publikacji:

2017

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

low flow
K-means method
nonhierarchical cluster analysis

Opis:

In the paper the possibility of using statistical method for data agglomeration, i.e. nonhierarchical cluster analysis for low flow grouping was made. The study material included daily flows from the multi-year period of 1963–1983 collected for 19 catchments, located in the upper Vistula basin. Regions with the same flow were determined with the use of nonhierarchical cluster analysis (K-means). Groups were characterized by low flow and selected physiographic and meteorological features of the catchments. The procedure of catchments assigning to the clusters was started from two clusters and finished at five. The next moving and assigning of catchments into clusters resulted in a cluster in which there was only one catchment (for five clusters). Another objects’ delineation did not give an objective effects, based on which it was difficult to determine a clear criterion of assigning each catchments into the clusters. The last step involved development of the models reflecting correlation and regression relationships. The identified clusters comprised catchments similar in terms of unit runoff, watercourse length, mean precipitation, median altitude, mean catchment slope, watercourse staff gauge zero, area covered by coniferous forests, arable lands, and soils.

Źródło:

Journal of Ecological Engineering; 2017, 18, 2; 44-50
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Porównanie wydajności algorytmu k-means zaimplementowanego w języku X10 i środowisku C++/MPI
Performance comparison of the k-means algorithm implemented in the X10 programming language and the C++/MPI environment

Autorzy:

Wyrzykowski, R.
Karoń, T.

Powiązania:

https://bibliotekanauki.pl/articles/91405.pdf

Data publikacji:

2016

Wydawca:

Warszawska Wyższa Szkoła Informatyki

Tematy:

algorytm k-średnich
język programowania X10
środowisko C++/MPI
porównanie
k-means algorithm
X10 programming language
C++/MPI environment
comparison

Opis:

W pracy opisano algorytm k-średnich oraz sposób jego implementacji w języku X10. Dokonano porównania tego rozwiązania z implementacją w języku C++11 z wykorzystaniem standardu MPI. Stwierdzono, że implementacja w języku X10 jest szybsza przy większej liczbie procesorów realizujących obliczenia niż implementacja w środowisku C++/MPI. Kod zapisany w języku X10 jest o 59% krótszy od kodu dla kombinacji C++/MPI.
In this work the k-means algorithm and the way of its implementation in the X10 programming language are described. The achieved results are compared with the implementation of the same algorithm in the C++11 programming language using the MPI standard. It was confirmed that the implementation in the X10 programming language is faster on a large number of processors than the implementation in the C++/MPI environment. Additionally, the X10 code is about 59% shorter than the code for the C++/MPI combination.

Źródło:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2016, 10, 14; 7-35
1896-396X
2082-8349

Pojawia się w:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

Geodesic distances for clustering linked text data

Autorzy:

Tekir, S.
Mansmann, F.
Keimer, D.

Powiązania:

https://bibliotekanauki.pl/articles/91737.pdf

Data publikacji:

2012

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

clustering
geodesic distance
text data
k-means algorithm
cosine distance
k-harmonic means
microprecision values

Opis:

The quality of a clustering not only depends on the chosen algorithm and its parameters, but also on the definition of the similarity of two respective objects in a dataset. Applications such as clustering of web documents is traditionally built either on textual similarity measures or on link information. Due to the incompatibility of these two information spaces, combining these two information sources in one distance measure is a challenging issue. In this paper, we thus propose a geodesic distance function that combines traditional similarity measures with link information. In particular, we test the effectiveness of geodesic distances as similarity measures under the space assumption of spherical geometry in a 0-sphere. Our proposed distance measure is thus a combination of the cosine distance of the term-document matrix and some curvature values in the geodesic distance formula. To estimate these curvature values, we calculate clustering coefficient values for every document from the link graph of the data set and increase their distinctiveness by means of a heuristic as these clustering coefficient values are rough estimates of the curvatures. To evaluate our work, we perform clustering tests with the k-means algorithm on a subset of the EnglishWikipedia hyperlinked data set with both traditional cosine distance and our proposed geodesic distance. Additionally, taking inspiration from the unified view of the performance functions of k-means and k-harmonic means, min and harmonic average of the cosine and geodesic distances are taken in order to construct alternate distance forms. The effectiveness of our approach is measured by computing microprecision values of the clusters based on the provided categorical information of each article.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 3; 247-258
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

A proposal of a new method of choosing starting points for k-means grouping
Propozycja nowej metody wyboru punktów startowych do grupowania metodą k-średnich

Autorzy:

Korzeniewski, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/907035.pdf

Data publikacji:

2008

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Tematy:

cluster analysis
starting points
silhouette indices
k-means method

Opis:

When one groups set elements with the help of k-means it is crucial to choose starting points properly. If they are chosen incorrectly one may arrive at badly grouped elements. In the paper a new method of choosing starting points is proposed. It is based on the distance matrix only. Starting points are chosen so as to improve the classical method of choosing points which are as far from one another as possible. The quality of grouping is assessed by means of silhouette indices — it is compared with the quality of grouping done with randomly chosen starting points and with maximum distance interval method. Sets from Euclidean spaces are generated with the help of CLUSTGEN software written by J. Milligana.
Gdy grupujemy punkty zbioru metodą k-średnich to zasadniczym problemem jest właściwy wybór punktów startowych. Jeśli są one źle wybrane to grupowanie może być złe. W artykule zaproponowana jest nowa metoda wyboru punktów startowych. Metoda ta jest oparta wyłącznie na znajomości macierzy odległości. Punkty startowe są wybierane tak, by poprawić wybór, który otrzymamy przy pomocy metody klasycznej polegającej na wyborze punktów możliwie jak najbardziej od siebie oddalonych. Jakość grupowania jest oceniana przy pomocy indeksów sylwetkowych - porównywana jest z jakością grupowania otrzymanego przy losowym wyborze punktów startowych oraz przy wyborze metodą klasyczną. Zbiory z przestrzeni euklidesowych są generowane przy pomocy programu CLUSTGEN autorstwa J. Milligana.

Źródło:

Acta Universitatis Lodziensis. Folia Oeconomica; 2008, 216
0208-6018
2353-7663

Pojawia się w:

Acta Universitatis Lodziensis. Folia Oeconomica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

Decision-making enhancement in a big data environment : application of the K-means algorithm to mixed data

Autorzy:

Koren, Oded
Hallin, Carina Antonia
Perel, Nir
Bendet, Dror

Powiązania:

https://bibliotekanauki.pl/articles/91712.pdf

Data publikacji:

2019

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

big data
mixed data
hadoop
K-means
decision making

Opis:

Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 4; 293-302
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

Data Mining Application in Air Transportation – the Case of Turkish Airlines

Autorzy:

Pisarek, Renata
Akpinar, Musab Talha
Hızıroglu, Abdulkadir

Powiązania:

https://bibliotekanauki.pl/articles/504638.pdf

Data publikacji:

2017

Wydawca:

Międzynarodowa Wyższa Szkoła Logistyki i Transportu

Tematy:

data mining
K-means
airlines
air transport
Turkish Airlines

Opis:

The paper presents an exemplification of data mining techniques in aviation industry on the basis of Turkish Airlines. The purpose of the paper is to present application of data mining on the selected operational data, concerning international flight passenger baggage data, in year 2015. The differences in passenger and flight profiles have been examined. Firstly, two-steps approach allowed defining the number of clusters. Secondly, K-means clustering were applied to divide data into a certain number of clusters representing the different areas of consumption. Results can contribute to higher efficiency in decision making regarding destination offer and fleet management.

Źródło:

Logistics and Transport; 2017, 36, 4; 79-88
1734-2015

Pojawia się w:

Logistics and Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks

Autorzy:

Jacob, Joshua
Shinde, Sumedha
Narayan, D. G.

Powiązania:

https://bibliotekanauki.pl/articles/27312951.pdf

Data publikacji:

2023

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

clustering
controller placement
PAM
K-means++
silhouette score
SDN

Opis:

Software defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and ensures programmable network management. In SDN, the control plane is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts the performance of SDN. In this work, we utilize clustering techniques to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. The evaluation involves the use of the Mininet emulator with POX as the SDN controller. By employing the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the best location for placing the controller within each cluster. The proposed approach is compared with existing works in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.

Źródło:

Journal of Telecommunications and Information Technology; 2023, 4; 9--17
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Supporting investment decisions using data mining methods

Autorzy:

Sysiak, W.
Trajer, J.
Janaszek, M.

Powiązania:

https://bibliotekanauki.pl/articles/93017.pdf

Data publikacji:

2009

Wydawca:

Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach

Tematy:

data mining
decision support
k-means clustering
neural networks

Opis:

This paper presents an application of k-means clustering in preliminary data analysis which preceded the choice of input variables for the system supporting the decision about stock purchase or sale on capital markets. The model forecasting share prices issued by companies in the food-processing sector quoted at the Warsaw Stock Exchange was created in STATISTICA 7.1. It was based on neural modeling and allowed for the assessment of changes direction in securities values (increase, decrease) and generates the quantitative forecast of their future price.

Źródło:

Studia Informatica : systems and information technology; 2009, 1(12); 67-78
1731-2264

Pojawia się w:

Studia Informatica : systems and information technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

Implementation of Big Data Concept for Variability Mapping Control of Financing Assessment of Informal Sector Workers in Bogor City

Autorzy:

Salmah, Salmah
Andria, Fredi
Wahyudin, Irfan

Powiązania:

https://bibliotekanauki.pl/articles/1065325.pdf

Data publikacji:

2019

Wydawca:

Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN

Tematy:

Big Data
Cluster
Informal Worker Sector
K-Means Clustering

Opis:

At present risks and uncertainties occur in protecting health for the community. This requires a national health insurance program that can guarantee health care costs. One of the program participants is a resident who works in the informal sector. This group is vulnerable as well as the potential for the implementation of health insurance programs. However, the level of participation of informal sector workers is still low, so an analysis of the constraints affecting it is needed. This study aims to identify categories of informal sector workers and analyze various obstacles faced by informal sector workers to become health insurance participants in the city of Bogor. The method used is the concept of big data with K-means clustering data mining techniques to group informal sector workers along with the constraints that exist in each of these groups. The results showed that there were 3 clusters with very low Social Security Administrator (BPJS) health ownership, namely cluster 1, cluster 3, and cluster 5. Each cluster had different constraints. Cluster 1 has constraints on the number of dependents it has, Cluster 3 has constraints on the gender side that are dominated by women, while Cluster 5 has constraints on the low-income side. Each cluster has a different obstacle resolution recommendation, namely for cluster 1 by registering workers in JKN contribution recipient (PBI) participants, cluster 2 by giving outreach to women who have only focused on men, and for clusters 5 by involving the community as a forum for the empowerment of informal sector workers.

Źródło:

World Scientific News; 2019, 135; 261-282
2392-2192

Pojawia się w:

World Scientific News

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

Employment and economic entities in the Polish financial sector from 2005-2016
Zatrudnienie i podmioty ekonomiczne w polskim sektorze finansowym w latach 2005-2016

Autorzy:

Grzywińska-Rąpca, Małgorzata
Markowski, Lesław

Powiązania:

https://bibliotekanauki.pl/articles/425058.pdf

Data publikacji:

2018

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

financial sector
unemployment
economic entities
k-means method
trends

Opis:

The article analyzes employment in the financial sector and entities conducting financial, insurance or other activities. The aim of this study is to examine employment in the financial sector at the level of provinces and registered entities of this sector using multidimensional methods of statistical analysis. The results of the classification indicate the geographical division of the country in terms of the number of financial and insurance companies. However, the high slope of the directional coefficient means a very strong, growing tendency for the Mazowieckie voivodship, characterized by a much slower trend for the Dolnośląskie, Pomorskie and Śląskie voivodships. In fact, for most of the provinces, trends indicate a statistically significant, negative development trend for the analyzed phenomenon from 2005-2016.

Źródło:

Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 1; 79-93
1507-3866

Pojawia się w:

Econometrics. Ekonometria. Advances in Applied Data Analytics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

Wielowymiarowa analiza porównawcza jako narzędzie oceny spółek deweloperskich notowanych na GPW
Multivariate comparative analysis as a toolto evaluate the development of companies listed on the Warsaw Stock Exchange

Autorzy:

Chrzanowska, Mariola
Zielińska-Sitkiewicz, Monika

Powiązania:

https://bibliotekanauki.pl/articles/425133.pdf

Data publikacji:

2013

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

Ward’s method
k-means method
Polish developer companies

Opis:

The diversity and multiplicity of information associated with investment in the stock market can cause problems with the proper understanding of the analyzed phenomena. In particular it refers to small investors who invest directly in stocks. Therefore, evaluating the financial condition of listed companies is very important, hence the need to use methods that will simplify and thus make stock market analysis easier. This paper presents an attempt to apply the selected financial ratios for the classification of 17 real estate companies listed on the Warsaw Stock Exchange into groups characterized by a similar economic condition. In the study multidimensional comparative analysis was used, i.e. Ward’s method and the method of k-means. The analysis was carried out in the period 2010-2012. In the experiment it was proved that using Ward’s method could identify companies with the weakest condition.

Źródło:

Econometrics. Ekonometria. Advances in Applied Data Analytics; 2013, 4(42); 60-71
1507-3866

Pojawia się w:

Econometrics. Ekonometria. Advances in Applied Data Analytics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

An application of machine learning methods to cutting tool path clustering and rul estimation in machining

Autorzy:

Zegarra, Fabio C.
Vargas-Machuca, Juan
Roman-Gonzalez, Avid
Coronado, Alberto M.

Powiązania:

https://bibliotekanauki.pl/articles/28407324.pdf

Data publikacji:

2023

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

feature extraction
k-means clustering
time series
unsupervised learning

Opis:

Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools. However, the data used in many cases correspond to simple and stable processes that differ from practical applications. In this work, a novel dataset consisting of eight cutting tools with complex tool paths is used. The time series of the tool paths, corresponding to the three-dimensional position of the cutting tool, are grouped according to their shape. Three unsupervised clustering techniques are applied, resulting in the identification of DBA-k-means as the most appropriate technique for this case. The clustering process helps to identify training and testing data with similar tool paths, which is then applied to build a simple two-feature prediction model with the same level of precision for RUL prediction as a more complex four-feature prediction model. This work demonstrates that by properly selecting the methodology and number of clusters, tool paths can be effectively classified, which can later be used in prediction problems in more complex settings.

Źródło:

Journal of Machine Engineering; 2023, 23, 4; 5--17
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

Autorzy:

Shehab, Jinan N.
Abdulkadhim, Hussein A.

Powiązania:

https://bibliotekanauki.pl/articles/1844494.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

change detection
k-means clustering
multitemporal satellite image
PSO
Gabor wavelet filter
remote sensing

Opis:

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.

Źródło:

International Journal of Electronics and Telecommunications; 2021, 67, 3; 403-408
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

Methods for imputation of missing values and their influence on the results of segmentation research
Metody uzupełniania braków danych i ich wpływ na wyniki badań segmentacyjnych.

Autorzy:

Gąsior, Marcin
Skowron, Łukasz

Powiązania:

https://bibliotekanauki.pl/articles/425241.pdf

Data publikacji:

2016

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

missing values
cluster analysis
k-means algorithm
k-medoids algorithm

Opis:

The lack of answers is a common problem in all types of research, especially in the field of social sciences. Hence a number of solutions were developed, including the analysis of complete cases or imputations that supplement the missing value with a value calculated according to different algorithms. This paper evaluates the influence of the adopted method for the supplementation of missing answers regarding the result of segmentation conducted with the use of cluster analysis. In order to achieve this we used a set of data from an actual consumer research in which the cases with missing values were deleted or supplemented with the use of various methods. Cluster analyses were then performed on those sets of data, both with the assumption of ordinal and ratio level of measurement, and then the grouping quality, as expressed by different indicators, was evaluated. This research proved the advantage of imputation over the analysis of complete cases, it also proved the validity of using more complex approaches than the simple supplementation with an average or median value.

Źródło:

Econometrics. Ekonometria. Advances in Applied Data Analytics; 2016, 4 (54); 61-71
1507-3866

Pojawia się w:

Econometrics. Ekonometria. Advances in Applied Data Analytics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Document Clustering : Concepts, Metrics and Algorithms

Autorzy:

Tarczynski, T.

Powiązania:

https://bibliotekanauki.pl/articles/226231.pdf

Data publikacji:

2011

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

document clustering
text mining
k-means
hierarchical clustersting
vector space model

Opis:

Document clustering, which is also refered to as text clustering, is a technique of unsupervised document organisation. Text clustering is used to group documents into subsets that consist of texts that are similar to each orher. These subsets are called clusters. Document clustering algorithms are widely used in web searching engines to produce results relevant to a query. An example of practical use of those techniques are Yahoo! hierarchies of documents [1]. Another application of document clustering is browsing which is defined as searching session without well specific goal. The browsing techniques heavily relies on document clustering. In this article we examine the most important concepts related to document clustering. Besides the algorithms we present comprehensive discussion about representation of documents, calculation of similarity between documents and evaluation of clusters quality.

Źródło:

International Journal of Electronics and Telecommunications; 2011, 57, 3; 271-277
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Development of Data-mining Technique for Seismic Vulnerability Assessment

Autorzy:

Wojcik, Waldemar
Karmenova, Markhaba
Smailova, Saule
Tlebaldinova, Aizhan
Belbeubaev, Alisher

Powiązania:

https://bibliotekanauki.pl/articles/1844631.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

data analysis
seismic assessment
clustering
h-means
k-means
random forest

Opis:

Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today’s machine learning methods allow the use of “fast” methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.

Źródło:

International Journal of Electronics and Telecommunications; 2021, 67, 2; 261-266
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Semi-automated classification of landform elements in armenia based on srtm dem using k-means unsupervised classification

Autorzy:

Piloyan, Artak
Konečný, Milan

Powiązania:

https://bibliotekanauki.pl/articles/1052490.pdf

Data publikacji:

2017-03-15

Wydawca:

Uniwersytet im. Adama Mickiewicza w Poznaniu

Opis:

Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

Źródło:

Quaestiones Geographicae; 2017, 36, 1; 93-103
0137-477X
2081-6383

Pojawia się w:

Quaestiones Geographicae

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Segmentacja facebookowiczów - ujęcie ilościowe
Segmentation of Facebook users – quantification

Autorzy:

Czerska, Iwona

Powiązania:

https://bibliotekanauki.pl/articles/419807.pdf

Data publikacji:

2015

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

segmentation
Facebook users
Ward’s method
k-means method
segments profiling

Opis:

The purpose of this study is the statistical analysis of results of the online survey in order to make market segmentation of Facebook users. Non-random sampling methods were used: convenience sampling and snowball sampling method. Because the sample was not representative, it did not give rise to statistical inferences about the population of Facebook users. The survey results were only part of the initial diagnosis, a description of the existing state of affairs. On the basis of selected market segmentation criteria and using the generalized k-means clustering algorithm and the Ward agglomeration method three clusters were formed: “Informed over-cautious persons” (56,31% of the sample), “Committed risktakers” (20,39% of the sample) and “Persistent assertive people” (23,30% of the sample). Segments were profiled on the basis of psychographic and behavioral criteria. The statistical significance of the relationship between clusters of Internet users and individual variables was confirmed by the chi-square test.

Źródło:

Nauki o Zarządzaniu; 2015, 3 (24); 33-40
2080-6000

Pojawia się w:

Nauki o Zarządzaniu

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

Machine Learning-Based Small Cell Location Selection Process

Autorzy:

Wasilewska, Małgorzata
Kułacz, Łukasz

Powiązania:

https://bibliotekanauki.pl/articles/1839352.pdf

Data publikacji:

2021

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

base station selection
k-means clustering
spectral clustering
user equipment allocation

Opis:

In this paper, the authors present an algorithm for determining the location of wireless network small cells in a dense urban environment. This algorithm uses machine learning, such as k-means clustering and spectral clustering, as well as a very accurate propagation channel created using the ray tracing method. The authors compared two approaches to the small cell location selection process – one based on the assumption that end terminals may be arbitrarily assigned to stations, and the other assuming that the assignment is based on the received signal power. The mean bitrate values are derived for comparing different scenarios. The results show an improvement compared with the baseline results. This paper concludes that machine learning algorithms may be useful in terms of small cell location selection and also for allocating users to small cell base stations.

Źródło:

Journal of Telecommunications and Information Technology; 2021, 2; 120-126
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 34.

Tytuł:

Speaker Model Clustering to Construct Background Models for Speaker Verification

Autorzy:

Dişken, G.
Tüfekci, Z.
Çevik, U.

Powiązania:

https://bibliotekanauki.pl/articles/177299.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

Gaussian mixture models
k-means
imposter models
speaker clustering
speaker verification

Opis:

Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model representations for speaker verification purpose. First, a UBM is trained, and speaker models are adapted from the UBM. Then, the k-means algorithm with the Euclidean distance measure is applied to the speaker models. The speakers are divided into two, three, four, and five clusters. The resulting cluster centers are used as background models of their respective speakers. Experiments showed that the proposed method consistently produced lower Equal Error Rates (EER) than the conventional UBM approach for 3, 10, and 30 seconds long test utterances, and also for channel mismatch conditions. The proposed method is also compared with the i-vector approach. The three-cluster model achieved the best performance with a 12.4% relative EER reduction in average, compared to the i-vector method. Statistical significance of the results are also given.

Źródło:

Archives of Acoustics; 2017, 42, 1; 127-135
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

Identification of Homogeneous Regions of Specific Minimum Flows in the State of Goiás, Brazil

Autorzy:

Basso, Raviel
Santana, Kássia
Honório, Michelle
Costa, Isabella
Leitão, Sanderson
Albuquerque, Antonio
Scalize, Paulo

Powiązania:

https://bibliotekanauki.pl/articles/24201732.pdf

Data publikacji:

2023

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

reference flow
permanence curve
K-means method
flow regionalisation
water management

Opis:

Hydrological information is essential for adequate water resources management as well as for water supply, energy supply, water allocation, among other services. However, this information does not always exist in quantity and quality to be used in hydrological or water management studies, and alternative methods are required to estimate minimum flows. Estimation based on homogeneous regions enables to transfer observation data from a known location to a location without data, but in the same region. Since the fluviometric stations in the state of Goiás (Brazil) are not uniformly distributed, the present work aimed at delimiting homogeneous regions of minimum flows, using the cluster grouping method with the K-means algorithm.Thus, 71 fluviometric stations with at least 5 years of continuous data were selected, obtained from the HIDROWEB system. In addition to the observed data, other variables were considered, such as drainage area, perimeter, specific minimum flows Q7,10, Q90, Q95 and average slope. The use of all these variables together with the observed data made it possible to determine,with great accuracy, 5 homogeneous regions of minimum flows based on the cluster analysis, enabling to obtain the minimum flows of reference for each region.In the selected homogeneous regions, it was possible to observe that the regions with the highest values of average slope presented smaller minimum flows, and the same could be observed under inverse conditions, i.e., lower values of average slope had higher minimum flows.It is also noteworthy that river monitoring is deficient in the center-south and center-north parts of the state of Goiás, making water resources management difficult. This fact indicates, therefore, the need to expand the river monitoring system throughout the state, especially in its southern and northern regions.

Źródło:

Journal of Ecological Engineering; 2023, 24, 4; 357--367
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Identification of road safety measures by elderly pedestrians based on K-Means clustering and hierarchical cluster analysis

Autorzy:

Leonardi, Salvatore
Distefano, Natalia
Pulvirenti, Giulia

Powiązania:

https://bibliotekanauki.pl/articles/1833631.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

road traffic
safety measures
road safety
cluster analysis
human factor
vulnerable users
elderly pedestrians
ruch drogowy
środki bezpieczeństwa
bezpieczeństwo na drogach
analiza skupień
czynnik ludzki
wrażliwi użytkownicy

Opis:

Introduction: Pedestrians aged over 65 are known to be a critical group in terms of road safety because they represent the age group with the highest number of fatalities or injured people in road accidents. With a current ageing population throughout much of the developed world, there is an imminent need to understand the current transportation requirements of older adults, and to ensure sustained safe mobility and healthy. Objectives: The aim of this study is to capture and analyze the key components that influence the identification of design solutions and strategies aimed at improving the safety of pedestrian paths for elderly. Method: A survey was conducted in 5 different locations in Catania, Italy. The locations were specifically chosen near to attraction poles for elderly pedestrians (e.g. centers for the elderly, squares, churches). Participants were recruited in person, so as to select exclusively people over 70. The sample comprised 322 participants. Both Hierarchical and K-Means clustering were used in order to explore which solutions elderly pedestrian propose for improving the safety of pedestrian path. Results: The results show that the judgment expressed by the elderly on the solutions for improving pedestrian safety is linked to the gender, to the experience as road users, and to mobility and vision problems. All solutions proposed regard road infrastructure (improvement of pedestrian crossings and of sidewalks, implementation of traffic calming measures, improvement of lighting), except for police supervision. Conclusion: This study has identified the factors that influence the identification of the best solutions to increase the safety level of pedestrian paths for elderly people. The aspects related to human factors considered were the gender, the factors associated with the experience as road users and the factors related to age related problems (mobility, vision and hearing problems). The results of this research could support traffic engineers, planners, and decision-makers to consider the contributing factors in engineering measures to improve the safety of vulnerable users such as elderly pedestrians.

Źródło:

Archives of Transport; 2020, 56, 4; 107-118
0866-9546
2300-8830

Pojawia się w:

Archives of Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

New Method of Variable Selection for Binary Data Cluster Analysis

Autorzy:

Korzeniewski, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/466036.pdf

Data publikacji:

2016

Wydawca:

Główny Urząd Statystyczny

Tematy:

cluster analysis
market segmentation
selection of variables
binary data
k-means grouping

Opis:

Cluster analysis of binary data is a relatively poorly developed task in comparison with cluster analysis for data measured on stronger scales. For example, at the stage of variable selection one can use many methods arranged for arbitrary measurement scales but the results are usually of poor quality. In practice, the only methods dedicated for variable selection for binary data are the ones proposed by Brusco (2004), Dash et al. (2000) and Talavera (2000). In this paper the efficiency of these methods will be discussed with reference to the marketing type data of Dimitriadou et al. (2002). Moreover, the primary objective is a new proposal of variable selection method based on connecting the filtering of the input set of all variables with grouping of sets of variables similar with respect to similar groupings of objects. The new method is an attempt to link good features of two entirely different approaches to variable selection in cluster analysis, i.e. filtering methods and wrapper methods. The new method of variable selection returns best results when the classical k-means method of objects grouping is slightly modified.

Źródło:

Statistics in Transition new series; 2016, 17, 2; 295-304
1234-7655

Pojawia się w:

Statistics in Transition new series

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Cluster analysis of effectiveness of labour market policy in the European Union

Autorzy:

Rollnik-Sadowska, Ewa
Dąbrowska, Edyta

Powiązania:

https://bibliotekanauki.pl/articles/18800477.pdf

Data publikacji:

2018

Wydawca:

Instytut Badań Gospodarczych

Tematy:

labour market policy expenditure
effectiveness
efficiency
Ward’s method
k-means method

Opis:

Research background: In the era of demographic changes and the need for rationalization of public expenditure, the European Union social policy promotes the activation approach. In addition, a growing importance of increasing the effectiveness and efficiency of public entities can be noticed. These phenomena are visible in the implementation of the labour market policy. However, the EU countries represent a different approach to spending public funds on issues related to the implementation of  labour market policy. Purpose of the article: The authors are presenting the main theoretical assumptions concerning effectiveness and efficiency of labour market policy. Moreover, in the paper the EU countries are classified in clusters according to their level of expenditure on different categories of LMP. A comparison of the situation over ten years - in 2004 and 2014 - has also been conducted. In 2004, ten new members entered the EU, and the year 2014 presents the most current data in the analyzed area. Methods: As a research method cluster analysis was applied. Cross-country labour market situation throughout the EU is presented by the analysis of the Eurostat data. The countries are grouped in clusters following Ward's and k-means methods. Findings & Value added: There is a need to work out a complex evaluation of labour market policies in the EU to provide comparative analysis of the EU countries (or groups of countries). It would allow to determine the level of development of the country in terms of the efficiency of labour market policies. The EU countries with the best labour market indicators represent diverse levels of LMP expenditure.

Źródło:

Oeconomia Copernicana; 2018, 9, 1; 143-158
2083-1277

Pojawia się w:

Oeconomia Copernicana

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

BADANIE PRZESTRZENNEGO ZRÓŻNICOWANIA POZIOMU EKOTURYSTYKI W POLSCE Z WYKORZYSTANIEM ANALIZY DYSKRYMINACYJNEJ
APPLICATION OF DISCRIMINANT ANALYSIS IN THE STUDY OF LEVEL OF DIVERSITY OF ECOTOURISM IN POLAND

Autorzy:

Bąk, Iwona

Powiązania:

https://bibliotekanauki.pl/articles/453583.pdf

Data publikacji:

2013

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki

Tematy:

ekoturystyka
analiza dyskryminacyjna
metoda k -średnich
ecotourism
discrimination analysis
k-means method

Opis:

Celem artykułu jest analiza przestrzennego zróżnicowania poziomu atrakcyjności podregionów w Polsce z punktu widzenia możliwości rozwoju w nich turystyki przyjaznej środowisku przyrodniczemu, tzw. ekoturystyki. Do analizy wykorzystano wskaźniki charakteryzujące atrakcyjność środowiska naturalnego podregionów (stymulanty) oraz wskaźniki mierzące poziom jego zanieczyszczenia (destymulanty). Klasyfikacji podregionów dokonano za pomocą analizy dyskryminacyjnej. Wstępnej klasyfikacji obiektów na grupy, a tym samym wyboru zmiennej grupującej, dokonano stosując metodę k-średnich.
The main goal of this paper is the analysis of the spatial differentiation of Poland

Źródło:

Metody Ilościowe w Badaniach Ekonomicznych; 2013, 14, 3; 7-16
2082-792X

Pojawia się w:

Metody Ilościowe w Badaniach Ekonomicznych

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

Tytuł:

Wspomaganie decyzji zakupowych w branży spawalniczej za pomocą metody K-średnich
Purchase decision-making support in the welding industry with the use of the k-means method

Autorzy:

Rogalewicz, M.
Kujawińska, A.

Powiązania:

https://bibliotekanauki.pl/articles/203222.pdf

Data publikacji:

2016

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

wspomaganie decyzji
analiza skupień
metoda k-średnich
decision support
clustering
k-means method

Opis:

Podejmowanie decyzji w przedsiębiorstwie wiąże się często z wyborem najlepszego rozwiązania na podstawie wielu kryteriów opisujących analizowany problem. Z tego punktu widzenia można go nazwać wielokryterialnym problemem decyzyjnym. W artykule przedstawiono zastosowanie jednej z metod wspomagania decyzji – analizy skupień metodą k-średnich – w doborze materiałów dodatkowych do procesu spawania metodą SAW. Dokonano podziału na skupienia, uwzględniając dwa kryteria doboru ich początkowych centrów, porównano oba warianty, a na końcu scharakteryzowano szczegółowo grupy wyodrębnione za pomocą jednego z nich. Wybrane podejście do analizy skupień okazało się przydatne we wspomaganiu decyzji dotyczących zakupów w branży spawalniczej.
Decision-making in enterprises is often connected with selecting the best solution on the basis of many criteria describing the analyzed problem. From this point of view, it can be called a multi–criterial decision–making problem. The article presents the use of a chosen clustering method – the k-means method – in the selection of materials for the SAW method process. Clusters were divided into two, based on the two different ways of choosing their initial centers. The two options were compared, and finally the clusters created on the basis of the chosen division were characterized in detail. The selected approach proved useful as decision-making support for purchasing materials in the welding industry.

Źródło:

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie; 2016, 70; 203-214
0239-9415

Pojawia się w:

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 41.

Tytuł:

The PROMETHEE II method in multi-criteria evaluation of cryptocurrency exchanges
Metoda PROMETHEE II w wielokryterialnej ocenie giełd kryptowalut

Autorzy:

Kądziołka, K.

Powiązania:

https://bibliotekanauki.pl/articles/2048732.pdf

Data publikacji:

2021

Wydawca:

Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej

Tematy:

k-means algorithm
hierarchical clustering
cryptocurrency exchanges
composite indicator
weighting scheme
PROMETHEE II

Opis:

Subject and purpose of work: The aim of this work is to present the application possibilities of PROMETHEE II method used to create a ranking of cryptocurrency exchanges as well as comparing the results of multi-criteria and multi-dimensional analysis. A simulation method for determining the weights of criteria is proposed, which maximizes the similarity of the final ranking to the other ones. Materials and methods: PROMETHEE II method and taxonomic measure were used to create rankings of exchanges. Hierarchical clustering combined with the k-means algorithm was used to identify groups of exchanges with a similar level of the values of net flows. Publicly available data published on the Internet were analysed. Results: There was a high consistency in the ordering of exchanges when a multi-criteria and a multi-dimensional approach were used. Four groups of exchanges with a similar level of the values of net flows were identified. Exchanges in group one were characterized by the highest average net flows. Conclusions: The multi-criteria approach can be used as an alternative to the multi-dimensional assessment of cryptocurrency exchanges. The proposed simulation method for determining the weights of criteria can be helpful in case the researcher has no information about the importance of the criteria.

Źródło:

Economic and Regional Studies; 2021, 14, 2; 131-145
2083-3725
2451-182X

Pojawia się w:

Economic and Regional Studies

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 42.

Tytuł:

The approach to supply chain cooperation in the implementation of sustainable development initiatives and companys economic performance

Autorzy:

Małys, Łukasz

Powiązania:

https://bibliotekanauki.pl/articles/22443111.pdf

Data publikacji:

2023

Wydawca:

Instytut Badań Gospodarczych

Tematy:

sustainable development
sustainable supply chain
sustainable development initiatives
corporate sustainability
k-means method

Opis:

Research background: The idea of sustainable development, in the face of the challenges encountered by contemporary society, is gaining increasing popularity. Currently, it recognizes the substantial role that companies play in its successful implementation. Initiatives in the field of sustainable development may be undertaken by companies independently as part of their own activities, or together with entities forming the supply chain as an element of sustainable supply chain management. Purpose of the article: Identification of groups of companies that are characterised by a different approach to cooperation in the field of sustainable development in the supply chain. Methods: The quantitative research was conducted in September 2020 with the use of the CATI (Computer-Assisted Telephone Interview) technique and a standardised survey questionnaire. A total of 500 randomly selected companies located in Poland participated in this study. The respondents were representatives of top management of the companies. In order to identify various groups of companies, a cluster analysis was performed using the k-means method in SPSS. Findings & value added: Based on the literature analysis, 3 areas of sustainable development have been identified, in which companies can become involved ? green design, sustainable operations, and reverse logistics & waste management. For each of the 3 areas, 3 clusters of companies were identified: companies that are not involved in sustainable development at all (1), companies that carry out most of the sustainable development initiatives independently (2), companies that carry out most of the sustainable development initiatives jointly with supply chain partners (3). The article also shows that the companies in different cluster differ in terms of perceived economic benefits achieved thanks to the implementation of sustainable development initiatives. This may suggest the need to develop separate sustainability solutions for such groups of companies in the future.

Źródło:

Equilibrium. Quarterly Journal of Economics and Economic Policy; 2023, 18, 1; 255-286
1689-765X
2353-3293

Pojawia się w:

Equilibrium. Quarterly Journal of Economics and Economic Policy

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 43.

Tytuł:

Application of Clustering Method in Different Geophysical Parameters for Researching Subsurface Environment
Zastosowanie metody klastrowania w różnych parametrach geofizycznych do badania środowiska podpowierzchniowego

Autorzy:

Le, Cuong Van Anh
Nguyen, Ngan Nhat Kim
Nguyen, Thuan Van

Powiązania:

https://bibliotekanauki.pl/articles/2172080.pdf

Data publikacji:

2022

Wydawca:

Polskie Towarzystwo Przeróbki Kopalin

Tematy:

Electrical Resistivity Imaging
MASW
K-means Clustering
obrazowanie oporności elektrycznej
grupowanie K-średnich

Opis:

Safety of construction needs knowledge of physical parameters as stiffness or porosity of the subsurface environment. Combination of different geophysical methods such as electrical resistivity imaging and multichannel analysis of surface waves can provide distributions of resistivity and shear velocity which are responsible for the underground physical parameters. Their joint interpretation can solve individual problems of none-uniqueness of the solutions when expressing two inversion results to describe environment characteristics. In our work, the k-means clustering method can categorize the two parameters into specific zones that can help to interpret the geophysical data effectively. Our workflow consists of two stages in which two independent geophysical data are inverted and the k-means clustering is applied to the two results for achieving the specified groups. The collocated geophysical data are measured in District 9, Ho Chi Minh City, Vietnam. Matching with the geology drillhole information, the joint results generally present layered medium with the upper zone having smaller resistivity and shear velocity values and the bottom zone of stronger stiffness.
Bezpieczeństwo konstrukcji wymaga znajomości parametrów fizycznych, takich jak sztywność czy porowatość środowiska podpowierzchniowego. Połączenie różnych metod geofizycznych, takich jak obrazowanie rezystywności elektrycznej i wielokanałowa analiza fal powierzchniowych, może dostarczyć rozkłady rezystywności i prędkości ścinania, które są odpowiedzialne za parametry fizyczne podziemnych warstw. Ich wspólna interpretacja może rozwiązać indywidualne problemy niejednoznaczności rozwiązań przy wyrażaniu dwóch wyników inwersji do opisu cech środowiska. W naszej pracy metoda grupowania k-średnich może podzielić dwa parametry na określone strefy, co może pomóc w skutecznej interpretacji danych geofizycznych. Nasz przepływ pracy składa się z dwóch etapów, w których dwa niezależne dane geofizyczne są odwracane, a grupowanie k-średnich jest stosowane do dwóch wyników w celu uzyskania określonych grup. Zebrane dane geofizyczne są mierzone w Dystrykcie 9, Ho Chi Minh City, Wietnam. Dopasowując się do informacji uzyskanych z odwiertów geologicznych, wyniki połączeń ogólnie przedstawiają ośrodek warstwowy, w którym górna strefa ma mniejsze wartości rezystywności i prędkości ścinania, a dolna strefa ma większą sztywność.

Źródło:

Inżynieria Mineralna; 2022, 2; 39--47
1640-4920

Pojawia się w:

Inżynieria Mineralna

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

Homogeneous regionalization via L-moments for Mumbai City, India

Autorzy:

Parchure, Amit Sharad
Gedam, Shirish Kumar

Powiązania:

https://bibliotekanauki.pl/articles/108584.pdf

Data publikacji:

2019

Wydawca:

Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy

Tematy:

regional analysis
L-moments
tests for homogeneity
K-means clustering
principal components analysis

Opis:

This study identified homogeneous rainfall regions using a combination of cluster analysis and the L-moments approach. The L-moments of heavy rainfall events of various durations (0.25, 1, 6, 12, 24, 48, 72, 96, and 120 h) were analysed using seasonal (June-September) rainfall measurements at 47 meteorological stations over the period 2006- 2016. In the primary phase of this study, the homogeneity of Mumbai as a single region was examined by statistical testing (based on L-moment ratios and variations of the L-moments). The K-means clustering approach was applied to the site characteristics to identify candidate regions. Based on the most appropriate distribution, these regions were subsequently tested using at-site statistics to form the final homogeneous regions. For durations above 1h, the regionalisation procedure delineated six clusters of similarly behaved rain gauges, where each cluster represented one separate class of variables for the rain gauges. However, for durations below 1h, the regionalisation procedure was not efficient in the sense of identifying homogeneous regions for rainfall. Furthermore, the final clusters confirmed that the spatial variation of rainfall was related to the complex topography, which comprised flatlands (below or at mean sea level), urban areas with high rise buildings, and mountainous and hilly areas.

Źródło:

Meteorology Hydrology and Water Management. Research and Operational Applications; 2019, 7, 2; 73-83
2299-3835
2353-5652

Pojawia się w:

Meteorology Hydrology and Water Management. Research and Operational Applications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

Lung cancer detection using an integration of fuzzy K-Means clustering and deep learning techniques for CT lung images

Autorzy:

Prasad, J. Maruthi Nagendra
Chakravarty, S.
Krishna, M. Vamsi

Powiązania:

https://bibliotekanauki.pl/articles/2173683.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

fuzzy K-means
artificial neural networks
SVM
support vector machine
crow search optimization algorithm
algorytm rozmytych k-średnich
sztuczne sieci neuronowe
maszyna wektorów wspierających
algorytm optymalizacji wyszukiwania kruków

Opis:

Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139006
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Wykrywanie defektów z wykorzystaniem termografii aktywnej i algorytmu k-średnich
Detection of Defects Using Active Thermography and k-Means Algorithm

Autorzy:

Dudzik, Sebastian

Powiązania:

https://bibliotekanauki.pl/articles/275938.pdf

Data publikacji:

2019

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

algorytm k-średnich
wykrywanie defektów
termografia aktywna
k-means algorithm
defect detection
active thermography

Opis:

W pracy przedstawiono nową metodę wykrywania defektów materiałowych z wykorzystaniem termografii aktywnej. W celu zwiększenia kontrastu cieplnego dokonano przetwarzania wstępnego zarejestrowanej sekwencji termogramów metodami morfologii matematycznej. Do wykrywania defektów zastosowano algorytm k-średnich. W pracy zbadano wpływ miary odległości używanej w opisywanym algorytmie oraz doboru danych wejściowych na efektywność opisywanej metody. Eksperyment przeprowadzono dla próbki wykonanej z kompozytu zbrojonego włóknem węglowym (CFRP). W badaniach stwierdzono, że najmniejsze błędy wykrywania defektów za pomocą opisywanej metody uzyskuje się dla kwadratowej odległości euklidesowej.
The paper presents a new method of detecting material defects using active thermography. In order to increase the thermal contrast, preprocessing of the recorded sequence of thermograms was carried out using mathematical morphology methods. The k-means algorithm was used to detect defects. The work examined the impact of distance measure used in the described algorithm and the selection of input data on the effectiveness of the described method. The experiment was carried out for a sample made of carbon fiber reinforced composite (CFRP). Studies have shown that the smallest errors in defect detection using the described method are obtained for the square Euclidean distance.

Źródło:

Pomiary Automatyka Robotyka; 2019, 23, 3; 11-15
1427-9126

Pojawia się w:

Pomiary Automatyka Robotyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

Tytuł:

Ograniczenia analizy skupień metodą k-średnich w tworzeniu typologii obiektów
Limitations of k-means cluster analysis in creating typologies

Autorzy:

Wierzbiński, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/49390435.pdf

Data publikacji:

2009-12-08

Wydawca:

Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania

Opis:

Łatwość, z jaką za pomocą odpowiedniego programu komputerowego można dokonać skomplikowanych analiz danych, nie idzie niestety w parze ze zrozumieniem przez badaczy istoty wykonywanych analiz. Celem artykułu jest pokazanie ograniczeń analizy skupień (cluster analysis) metodą k-średnich, używanej powszechnie w badaniach marketingowych do segmentacji rynku, w psychologii do wykrywania różnic indywidualnych, stosowanej także do kategoryzacji krajów. Tok wywodu został zilustrowany wynikami własnych analiz wskaźników religijności w badaniach ISSP prób reprezentatywnych pochodzących z 32 krajów. Pokazano, że na podstawie tych samych danych możemy stworzyć za pomocą analizy skupień różne, spełniające kryteria statystyczne, kategoryzacje. Aby stworzyć użyteczną merytorycznie klasyfikację, trzeba testować różne warianty rozwiązań, pamiętając, że ważniejsza od istotności statystycznej, którą w przypadku analizy skupień można bardzo łatwo uzyskać, jest istotność merytoryczna.
The ease with which one can use computer program to conduct complex data analysis is not, unfortunately, coupled with the researchers' comprehension of the nature of the analyses being conducted. The aim of the article is to show the limitations of k-means cluster analysis, commonly used in market research to partition the consumers into segments, in psychology to detect individual differences, in cross-cultural research to categorize countries. The limitations of this method has been illustrated with the Author's own analyses of religiosity level in 32 countries based on the data collected by International Social Survey Program (ISSP). It was shown that, using k-means clustering of the same data, one could create different categorizations that fulfill statistical criteria. The Author points out recommendations that allow to get more valid results of cluster analysis keeping in mind that theoretical importance of the solution is equally or even more important than statistical significance.

Źródło:

Problemy Zarządzania; 2009, 7, 4(26); 224-233
1644-9584

Pojawia się w:

Problemy Zarządzania

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 48.

Tytuł:

Modele systemów podatkowych w państwach Unii Europejskiej
Tax System Models in the EU Countries

Autorzy:

Zielińska, Joanna
Sawulski, Jakub

Powiązania:

https://bibliotekanauki.pl/articles/2050108.pdf

Data publikacji:

2022

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

system podatkowy
analiza skupień
klasteryzacja
metoda k-średnich
tax system
clustering
k-means method

Opis:

Celem artykułu jest zidentyfikowanie podobieństw i różnic w systemach podatkowych państw Unii Europejskiej (UE) przez wyszczególnienie podstawowych modeli tych systemów. Dla jego osiągnięcia przeprowadzono analizę skupień metodą k-średnich, której podstawą było 12 parametrów charakteryzujących systemy podatkowe. W ten sposób wyodrębniono pięć modeli systemów podatkowych w państwach UE: zachodnioeuropejski, wschodnioeuropejski, nordycki, brytyjski i mieszany. Ich nazewnictwo wynika z tego, że podstawowe parametry systemu podatkowego są silnie skorelowane z położeniem geograficznym kraju. Prawdopodobnie zatem znaczący wpływ na ukształtowanie systemów podatkowych w państwach UE mają czynniki, takie jak historia, tradycja i kultura. Wyraźne różnice w konstrukcji systemów podatkowych są widoczne zwłaszcza między państwami tzw. starej piętnastki UE a państwami Europy Środkowo-Wschodniej.
The aim of the article is to identify the similarities and differences in the tax systems in the European Union (EU) countries by specifying the basic tax system models. For its implementation we carry out a cluster analysis using the k-means method based on 12 parameters characterising tax systems. We distinguish five models of tax systems in the EU countries: Western European, Eastern European, Nordic, British and mixed model. We use such a nomenclature as the basic parameters of the tax system are strongly correlated with the geographical location of the country. Probably factors such as history, tradition, and culture have a significant impact on the shapes of the tax systems in the EU. Clear differences exist especially between the EU-15 countries and Central and Eastern European countries.

Źródło:

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu; 2022, 66, 1; 168-181
1899-3192

Pojawia się w:

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 49.

Tytuł:

Zastosowanie metod taksonomicznych do analizy zużycia energii elektrycznej przez poszczególne województwa
The use of taxonomic methods for analysing electricity consumption by the individual provinces

Autorzy:

Tutak, M.

Powiązania:

https://bibliotekanauki.pl/articles/323203.pdf

Data publikacji:

2018

Wydawca:

Politechnika Śląska. Wydawnictwo Politechniki Śląskiej

Tematy:

energia elektryczna
sektor gospodarczy
metoda k-średnich
grupowanie
electricity
economic sector
k-means method
grouping

Opis:

Najważniejszym czynnikiem wpływającym z jednej strony na rozwój gospodarczy i dobrobyt społeczeństwa, a z drugiej na łagodzenie skutków jego ubóstwa jest energia elektryczna i jej dostępność. Rozwijająca się gospodarka kraju generuje coraz większe zapotrzebowanie na energię. Poszczególne województwa Polski charakteryzują się różnym stopniem wykorzystania energii elektrycznej w podstawowych sektorach ekonomicznych. Wykorzystanie energii elektrycznej uzależnione jest od wielu czynników, m.in. od stopnia uprzemysłowienia regionu, lokalizacji elektrowni, a także od liczby ludności i gospodarstw domowych. W artykule przedstawiono wyniki analizy porównawczej zużycia energii elektrycznej w poszczególnych województwach Polski z uwzględnieniem sektorów ekonomicznych. Do uzyskania klasyfikacji województw w zakresie wykorzystania energii elektrycznej w sektorach ekonomicznych wykorzystano metodę analizy wielowymiarowej, która przyporządkowuje województwa do odpowiednich grup (skupień) o zbliżonej ilości zużycia energii elektrycznej.
The most crucial factor influencing economic development and social well-being, as well as resulting in the mitigation of the effects of social poverty is electricity and its availability. The country’s growing economy generates an increasingly higher demand for energy. The individual Provinces of Poland have a different degree of electricity use in the basic economic sectors. The use of electricity is dependent on a number of factors, such as a given region’s degree of industrialisation, the locations of power plants, as well as the population and household numbers. The article presents the results of a comparative analysis of electricity consumption in the individual Provinces of Poland, with account being taken of the main economic sectors. The classification of the Provinces in terms of electricity use in economic sectors was performed by means of a multi-dimensional analysis method, which assigns the Provinces to appropriate groups (clusters) having similar quantities of electricity consumed.

Źródło:

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2018, 117; 675-686
1641-3466

Pojawia się w:

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 50.

Tytuł:

Shluková analýza a možnosti jejího využití při hledání typických skupin studentů během realizace výuky formou e-learningu
Cluster analysis and its application in search of typical groups of students in the implementation of teaching through e-learning

Autorzy:

CHRÁSKA, Miroslav

Powiązania:

https://bibliotekanauki.pl/articles/456627.pdf

Data publikacji:

2013

Wydawca:

Uniwersytet Rzeszowski

Tematy:

shluková analýza
shlukování metodou k-průměrů
studenti
e-learning
Cluster Analysis
K-Means Clustering
students

Opis:

Příspěvek popisuje výsledky výzkumu, v jehož rámci byly hledány typické skupiny studentů, které se objevují při realizaci výuky formou e-learningu. Využita byla shluková analýza, pomocí níž bylo zjištěno, že se vyskytuje pět charakteristických skupin studentů, které se odlišují zejména svým způsobem komunikace s tutorem.
This paper describes the results of research in which they were searched typical groups of students which appear in the implementation of teaching through e-learning. Cluster analysis was used. Was found that there are five characteristic groups of students, which is different especially in its own way communication with a tutor.

Źródło:

Edukacja-Technika-Informatyka; 2013, 4, 2; 147-153
2080-9069

Pojawia się w:

Edukacja-Technika-Informatyka

Dostawca treści:

Biblioteka Nauki

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