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Tytuł:
Możliwości wykorzystania statystycznych metod klasyfikacji danych w badaniach natężenia suburbanizacji
Scope of application of statistical methods of data classification in analysing the intensity of suburbanization
Autorzy:
Żelechowski, Maciej
Powiązania:
https://bibliotekanauki.pl/articles/1023252.pdf
Data publikacji:
2015-03-20
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
metropolisation
suburbanization
functional urban areas
statistical data classification
data clustering
metropolizacja
suburbanizacja
miejskie obszary funkcjonalne
klasyfikacja danych statystycznych
analiza skupień
Opis:
Suburbanizacja, jako składowa procesu metropolizacji, a jednocześnie jeden z etapów cyklu życia aglomeracji miejskich, dotyczy obecnie prawie wszystkich głównych ośrodków miejskich naszego kraju, stanowiąc podstawowy problem związany z ich rozwojem. Przydatnym narzędziem badania zróżnicowania natężenia suburbanizacji w strefach podmiejskich czy szerzej - na obszarach metropolitalnych - mogą być statystyczne metody klasyfikacji danych, takie jak analiza skupień. Metody te pozwalają na grupowanie jednostek terytorialnych danego obszaru (np. powiatów, gmin, miejscowości statystycznych) we względnie jednorodne klasy pod kątem natężenia zjawisk suburbanizacyjnych. Podstawą takiego grupowania jest podobieństwo pomiędzy poszczególnymi badanymi obiektami (np. gminami) w zakresie wartości zmiennych przyjmowanych za wskaźniki zaawansowania suburbanizacji.
Suburbanization, as a component of the process o f metropolisation, and on the other hand one of the stages of city life-cycle, is now observed in almost all main city agglomerations in our country, being the most important problem connected w ith their development. The useful tools of investigation of intensity of suburbanization in suburban zones, or wider - in metropolitan areas, can be the statistical methods of data classification, such as data clustering. These methods allow grouping the territorial units of given area (e.g. poviats, gminas, statistical locations) in relatively homogeneous classes as regards the intensity of suburbanization. The basis of such grouping is similarity between investigated objects (e.g. gminas) as regards the level of variables accepted for coefficients of intensity of suburbanization.
Źródło:
Rozwój Regionalny i Polityka Regionalna; 2015, 29; 139-148
2353-1428
Pojawia się w:
Rozwój Regionalny i Polityka Regionalna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recognition of lip prints using Fuzzy c-Means clustering
Autorzy:
Wrobel, K.
Froelich, W.
Powiązania:
https://bibliotekanauki.pl/articles/333981.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
lip print
image processing
clustering techniques
data classification
grafika wargowa
przetwarzanie obrazu
metody grupowania
klasyfikacja danych
Opis:
In this paper a new method for lip print recognition is proposed. The proposed approach is based on Fuzzy c-Means clustering of the characteristics features of lip prints. First, the Hough transform is applied for the recognition of the characteristic features within lip prints, then Fuzzy c-Means clustering is performed to cluster those features. The proposed algorithm applies the results of clustering to find an unknown image withing the collected repository of lip prints. Instead of comparing all pairs of individual characteristic features, the proposed algorithm uses the representatives of clusters for the comparison of images. The advantage of using the proposed method is its increased tolerance to the noise in data and thus the increased efficiency of the recognition. The effectiveness of presented method has been verified experimentally using real-world images. The results are satisfactory and suggest the possibility of using the method in forensic identification systems
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 67-73
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Reducing the number of paths in a minimized project-network with given bounds on the durations of activities
Autorzy:
Viattchenin, D.
Powiązania:
https://bibliotekanauki.pl/articles/406450.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
project-network
dominant path
heuristic possibilistic clustering
interval-valued data
stationary clustering structure
typical point
Opis:
This paper deals in a preliminary way with the problem of selecting the smallest possible number of dominant paths in a minimized project-network with given bounds on the permissible values of the durations of activities. For this purpose, a classification technique is proposed. This technique is based on a heuristic possibilistic clustering of interval-valued data. The basic concepts of heuristic possibilistic clustering are defined and methods for preprocessing interval-valued data are described. An illustrative example is considered in detail and some conclusions are formulated.
Źródło:
Operations Research and Decisions; 2015, 25, 4; 71-87
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-swarm that learns
Autorzy:
Trojanowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/969816.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
particle swarm optimization (PSO)
multi-swarm
dynamic optimization
memory
clusters
clustering evolving data streams
quantum particles
Opis:
This paper studies particle swarm optimization approach enriched by two versions of an extension aimed at gathering information during the optimization process. Application of these extensions, called memory mechanisms, increases computational cost, but it is spent to a benefit by incorporating the knowledge about the problem into the algorithm and this way improving its search abilities. The first mechanism is based on the idea of storing explicit solutions while the second one applies one-pass clustering algorithm to build clusters containing search experiences. The main disadvantage of the former mechanism is lack of good rules for identification of outdated solutions among the remembered ones and as a consequence unlimited growth of the memory structures as the optimization process goes. The latter mechanism uses other form of knowledge representation and thus allows us to control the amount of allocated resources more efficiently than the former one. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of optimized environments are discussed.
Źródło:
Control and Cybernetics; 2010, 39, 2; 359-375
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inefficiency of data mining algorithms and its architecture: with emphasis to the shortcoming of data mining algorithms on the output of the researches
Autorzy:
Tesema, Workineh
Powiązania:
https://bibliotekanauki.pl/articles/118221.pdf
Data publikacji:
2019
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
data mining
classification
clustering
association
regression
algorithms bottleneck
pozyskiwanie danych
klasyfikacja
grupowanie
asocjacja
regresja
wąskie gardło algorytmów
Opis:
This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used.
Źródło:
Applied Computer Science; 2019, 15, 3; 73-86
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Wyznaczanie wartości granicznych z wykorzystaniem metod grupowania danych
The determination of limit values using methods of data clustering
Autorzy:
Targosz, M.
Timofiejczuk, A.
Powiązania:
https://bibliotekanauki.pl/articles/257429.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Tematy:
wartości graniczne
grupowanie danych
modelowanie uszkodzeń
limits values
data clustering
failure modeling
Opis:
W artykule zaproponowano podejście do wyznaczenia wartości granicznych za pomocą algorytmów rozmytego grupowania danych. Wykorzystano algorytmy FCM, PCM oraz algorytm Gustafsona-Kessela. Eksperyment przeprowadzano na danych symulacyjnych. W tym celu zbudowano model numeryczny maszyny wirnikowej, symulującej określone stany i wielkości niewyważenia. Wyznaczone wartości graniczne porównano z wartościami otrzymanymi przy pomocy metody statystycznej. Wszystkie obliczenia wykonywano w środowisku Matlab-Simulink.
The paper describes a methodology for estimating the limit values of char-icteristics of diagnostic signals using methods of fuzzy data clustering (FCM, PCM and Gustafson-Kessel algorithms). The experiment was conducted on simulated data, using a numerical model of a rotor machine, simulating given inbalanced states. Limits were compared with value estimating using the statistical method.
Źródło:
Problemy Eksploatacji; 2011, 2; 213-221
1232-9312
Pojawia się w:
Problemy Eksploatacji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Advances in DGA-based diagnosis of power transformers - selected techniques
Postępy w diagnostyce transformatorów w oparciu o wyniki analizy chromatograficznej rozpuszczonych w oleju gazów - przegląd zastosowań nowych metod
Autorzy:
Szczepaniak, P. S.
Powiązania:
https://bibliotekanauki.pl/articles/257515.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Tematy:
diagnostyka
transformator
analiza chromatograficzna
gaz
obliczenie inteligentne
standard IEC
intelligent data analysis
clustering
classification
diagnosis of power transformers
Opis:
To a large extent, the chromatographic data obtained by measurements on power transformers reflect the state of a power transformer and allow the assessment of possible faults. The distribution of real learning data is not even approximately uniform and makes the partitioning of decision space difficult. The purpose of this paper is to present the results of the application of an EC-based classifier and a number of novel methods.
Jak wiadomo, wyniki analizy chromatograficznej gazów rozpuszczonych w oleju transformatorowym (Dissofoed Gas Analysis - DGA) mogą być użyte do diagnostyki transformatorów. Zwykle rozmieszczenie tych danych (ściśle ilorazów koncentracji wybranych gazów) w przestrzeni jest bardzo nierównomierne, a ponadto jednoznaczny podział tej przestrzeni na obszary decyzyjne o rozsądnej wielkości i liczbie jest bardzo trudny. Celem pracy jest dokonanie przeglądu zastosowań nowych metod, wśród nich tych mających korzenie w obliczeniach inteligentnych i odniesienie się do wyników uzyskiwanych za pomocą standardu EC (International Electrotechnical Commission).
Źródło:
Problemy Eksploatacji; 2011, 2; 189-199
1232-9312
Pojawia się w:
Problemy Eksploatacji
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
A new method for automatic determining of the DBSCAN parameters
Autorzy:
Starczewski, Artur
Goetzen, Piotr
Er, Meng Joo
Powiązania:
https://bibliotekanauki.pl/articles/1837535.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
clustering algorithms
DBSCAN
data mining
Opis:
Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 209-221
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel grid-based clustering algorithm
Autorzy:
Starczewski, Artur
Scherer, Magdalena M.
Książek, Wojciech
Dębski, Maciej
Wang, Lipo
Powiązania:
https://bibliotekanauki.pl/articles/2031101.pdf
Data publikacji:
2021
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
data mining
grid-based clustering
grid structure
Opis:
Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel grid-based algorithm which uses a method for an automatic determining of the number of grid cells. This method is based on the kdist function which computes the distance between each element of a dataset and its kth nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 4; 319-330
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data
Autorzy:
Singh, Juhi
Mittal, Mandeep
Powiązania:
https://bibliotekanauki.pl/articles/1841413.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
categorical data
clustering algorithm
frequent pattern mining
association rules
customer relationship management
Opis:
Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.
Źródło:
Management and Production Engineering Review; 2021, 12, 2; 57-64
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Clustering large-scale data based on modified affinity propagation algorithm
Autorzy:
Serdah, A. M.
Ashour, W. M.
Powiązania:
https://bibliotekanauki.pl/articles/91694.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
clustering
clustering algorithm
data clustering algorithm
propagation algorithm
Affinity Propagation
AP
klasteryzacja
algorytm klastrowania
algorytm propagacji
Opis:
Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 23-33
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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