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    Tytuł:
    Comparative analysis of proactive & reactive protocols for cluster based routing algorithms in WSNs
    Autorzy:
    Mohammed, Ibrahim Yahia
    Powiązania:
    https://bibliotekanauki.pl/articles/1062952.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Clustering based
    Energy
    LEACH
    Proactive
    Reactive
    Routing protocol
    Sensors
    TEEN
    WSNs
    Opis:
    Wireless Sensor Networks (WSNs) are networks that consist of sensors which are randomly deployed in inaccessible area to gathering data and transfer it to user or base station. The most important matters considered in sensor networks are efficient utilization of energy, network lifetime, and environmental conditions changes. To provide the communication facilities within the network a routing protocol is used. There are many techniques used to route data in sensor networks, clustering based is one of most common techniques used, cluster based routing protocols can be classified to proactive and reactive protocol depending on how source finds a route to destination. This paper is aim to study and compare the two mechanisms in cluster based routing by discuss different characteristics in each mechanism, and also analyzed the performance of two mechanisms by take two exemplar protocols LEACH and TEEN from proactive and reactive mechanism respectively in order to compare the performance in same scenario and simulation parameters.
    Źródło:
    World Scientific News; 2019, 124, 2; 131-142
    2392-2192
    Pojawia się w:
    World Scientific News
    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:
    The comparison of model-based clustering with heuristic clustering methods
    Autorzy:
    Witek, Ewa
    Powiązania:
    https://bibliotekanauki.pl/articles/657968.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    heurisic methods of clustering
    probability models
    model-based clustering
    Opis:
    Najczęściej w różnych analizach statystycznych wykorzystywane są klasyczne metody analizy skupień, opierające się na podejściu heurystycznym. W referacie zaprezentowane zostanie podejście modelowe w analizie skupień (model-based clustering), bazujące na modelach probabilistycznych. W części empirycznej referatu podejście to zostanie porównane z klasycznymi metodami taksonomicznymi (metodami hierarchicznymi oraz metodami iteracyjno- aglomeracyjnymi).
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2011, 255
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Teoria reakcji na pozycję w podejściu modelowym w takso- nomii
    Item response theory in model-based clustering
    Autorzy:
    Genge, Ewa
    Powiązania:
    https://bibliotekanauki.pl/articles/425062.pdf
    Data publikacji:
    2016
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    item response theory
    latent class analysis
    model-based clustering
    Opis:
    Item response theory is considered to be one of the two trends in methodological assessment of the reliability scale. In turn, latent class models can be viewed as a special case of model-based clustering, for heterogenous multivariate discrete data. We used the approach combining item response theory and latent class models to find groups of Polish households’ with similar saving ability levels. We analyzed data collected as part of the Polish Social Diagnosis using MultiLCIRT package of R.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2016, 1 (51); 9-19
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cloud computing migration barriers and advantages in manufacturing – an analysis of ambiguity and dependences in the assessment criteria
    Bariery i korzyści migracji do chmury obliczeniowej w przedsiębiorstwach przemysłowych – analiza niejednoznaczności i związków między kryteriami oceny
    Autorzy:
    Bartkiewicz, Witold
    Gontar, Zbigniew
    Powiązania:
    https://bibliotekanauki.pl/articles/425213.pdf
    Data publikacji:
    2018
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    cloud computing
    association mining
    similarity based semantic clustering
    Opis:
    There is still many misunderstandings associated with the assessment of the barriers encountered in the process of IT solutions migration to the computational cloud and the benefits to be expected. The purpose of this paper is to organize the criteria used in this field, to analyze the dependencies between them. This will allow us to better understand the problem of migration to the cloud and to improve the decision-making processes related to it. A comprehensive survey was carried out, covering about 400 manufacturing enterprises in Poland using cloud-based IT solutions in various areas. The elements of the study were questions about the barriers and benefits encountered in this process. The paper analyzes the relationships between the obtained categories using diverse data mining methods: association rules mining and hierarchical agglomerative clustering. The obtained results allow to identify the conceptual structure and build a model of relationships inside the problem.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 3; 41-54
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Poczucie śląskości wśród Ślązaków – analiza empiryczna z wykorzystaniem modeli klas ukrytych
    A sense of being Silesian – an empirical analysis with the use of latent class models
    Autorzy:
    Genge, Ewa
    Powiązania:
    https://bibliotekanauki.pl/articles/425295.pdf
    Data publikacji:
    2013
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    latent class analysis
    mixture model
    model-based clustering
    categorical data
    Opis:
    The paper focuses on latent class models and their application for quantitative data. Latent class modeling is one of multivariate analysis techniques of the contingency table and can be viewed as a special case of model-based clustering, for multivariate discrete data. It is assumed that each observation comes from one of the numbers of subpopulations, with its own probability distribution. We used latent class analysis for grouping and detecting homogeneity of Silesian people using poLCA package of R. We analyzed data collected by the Department of Social Pedagogy, University of Silesia in Katowice.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2013, 4(42); 48-59
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Teledetekcyjne wykorzystanie metody grupowania obiektów w oparciu o analizę gęstości
    Using object grouping method based on density analysis in remote sensing
    Autorzy:
    Wyczałek, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/341393.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Przyrodniczy we Wrocławiu
    Tematy:
    teledetekcja
    segmentacja obiektowa
    grupowanie gęstościowe
    remote sensing
    object segmentation
    density-based clustering
    Opis:
    Klasyczne podejście do klasyfikacji obiektów na obrazach teledetekcyjnych, zakładające rozpoznawanie szczegółów terenowych pogrupowanych w kilka kategorii tematycznych, reprezentowanych przez cechy radiometryczne kilkukanałowego obrazu rastrowego, nadal wymaga coraz bardziej zaawansowanych metod podnoszenia skuteczności. Spośród współcześnie wprowadzanych rozwiązań na szczególną uwagę zasługują metody określane mianem obiektowych, które bazują na analizach fragmentów obszaru mapy bitowej, pogrupowanych według określonych kryteriów homologiczności. Segmentację obrazu można uzyskać różnymi metodami, które rozwijane są w licznych dziedzinach zastosowań informatyki. W interpretacji obrazów teledetekcyjnych opracowano rozwiązania dostosowane do specyfiki tychże obrazów. W niniejszej pracy podjęto temat takiego szczególnego wykorzystania techniki grupowania obiektów spełniających określone kryteria dokładnościowe. Scharakteryzowano metodę gęstościową analiz baz danych i jej modyfikację dostosowaną do analiz obrazów rastrowych, a następnie podano proponowany sposób dalszego rozwoju metody z wykorzystaniem dostępnej informacji wektorowej. Wywód zilustrowano za pomocą uproszczonego modelu obrazu teledetekcyjnego.
    Typical approach to classification of objects on remote sensing images, which assume detection of terrain details being grouped into several thematic categories, represented by radiometric properties of the multispectral raster image, still needs more and more sophisticated methods of increasing efficiency. Among currently used solutions, on special attention claimed methods stated as the object-oriented, which are based on analyses of parts of the bitmap, grouped according to criteria of homogeneity. It is possible to carry out the image segmentation using various methods, which are developed within couple of informatics' sciences. In interpretation of remote sensing images there were elaborated approaches adapted to specific of those types of images. Such special use of grouping technique of objects fulfilled the particular accuracy criteria were investigated here. There were described the density-based clustering method of large databases and their implementation adapted to raster image analyses, and then, the possible solution using accessible vector information was suggested. The text is illustrated using the simplified model of remote sensing image.
    Źródło:
    Acta Scientiarum Polonorum. Geodesia et Descriptio Terrarum; 2005, 4, 1; 29-39
    1644-0668
    Pojawia się w:
    Acta Scientiarum Polonorum. Geodesia et Descriptio Terrarum
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An MST cluster analysis method under hesitant fuzzy environment
    Autorzy:
    Zhang, X.
    Xu, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/205680.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    hesitant fuzzy set
    minimal spanning tree
    graph theory-based clustering algorithm
    hesitant fuzzy distance
    Opis:
    Hesitant fuzzy sets (HFSs) are useful means to describe and deal with uncertain data. In this article, a minimal spinning tree (MST) algorithm based clustering technique under hesitant fuzzy environment is proposed. We first introduce the concepts of graph, MST, HFS, and hesitant fuzzy distance. Then, we present a hesitant fuzzy MST clustering algorithm to perform clustering analysis of HFSs via some hesitant fuzzy distances, and finally illustrate the effectiveness of our algorithm through two numerical examples.
    Źródło:
    Control and Cybernetics; 2012, 41, 3; 645-666
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On an Improvement of the Model-Based Clustering Method
    O pewnej modyfikacji w metodzie taksonomii opartej na modelach mieszanych
    Autorzy:
    Witek, Ewa
    Powiązania:
    https://bibliotekanauki.pl/articles/906293.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    Model-based clustering (MBC)
    Gaussian mixture models
    EM algorithm
    MLE
    MAP
    BIC
    conjugate prior
    Opis:
    W artykule przedstawiona została modyfikacja metody taksonomii opartej na modelach mieszanych, w przypadku gdy niemożliwym staje się oszacowanie parametrów modelu za pomocą algorytmu EM. Gdy liczba obiektów przypisanych do klasy jest mniejsza niż liczba zmiennych opisujących te obiekty, niemożliwym staje się oszacowanie parametrów modelu. By uniknąć tej sytuacji estymatory największej wiarygodności zastępowane są estymatorami o największym prawdopodobieństwie a posteriori. Wybór modelu o najlepszej parametryzacji i stosownej liczbie klas dokonywany jest wówczas za pomocą zmodyfikowanej statystyki BIC.
    An improvement o f the model-based clustering (MBC) method in the case when EM algorithm fails as a result o f singularities is the basic aim o f this paper. Replacement o f the maximum likelihood (MLE) estimator by a maximum a posteriori (MAP) estimator, also found by the EM algorithm is proposed. Models with different number o f components are compared using a modified version o f BIC, where the likelihood is evaluated at the MAP instead o f MLE. A highly dispersed proper conjugate prior is shown to avoid singularities, but when these are not present it gives similar results to the standard method o f MBC.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2009, 228
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Semantic segmentation and PSO based method for segmenting liver and lesion from CT images
    Autorzy:
    Nayantara, Vaidehi P.
    Surekha, Kamath
    Manjunath, K.N.
    Rajagopal, Kadavigere
    Powiązania:
    https://bibliotekanauki.pl/articles/2146955.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    liver lesion segmentation
    computed tomography
    semantic segmentation
    SegNet
    particle swarm optimization-based clustering
    Hounsfield Unit
    Opis:
    The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 3; 635--640
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction
    Autorzy:
    Pitchandi, Perumal
    Powiązania:
    https://bibliotekanauki.pl/articles/2201730.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    outliers removal
    culturalartefact objects
    3D reconstruction
    particle swarm optimization
    PSO
    spatial clustering
    density based spatial clustering
    noise clustering algorithm
    Opis:
    In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 269--281
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ant-based extraction of rules in simple decision systems over ontological graphs
    Autorzy:
    Pancerz, K.
    Lewicki, A.
    Tadeusiewicz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/330276.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    ant based clustering
    decision system
    DRSA
    ontological graphs
    rule extraction
    system decyzyjny
    grafy ontologiczne
    ekstrakcja reguł
    Opis:
    In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominance-based rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 2; 377-387
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of parameters of Gaussian mixture models by a hybrid method combining a self-adaptive differential evolution with the EM algorithm
    Estymacja parametrów modeli mieszanin rozkładów normalnych przy pomocy metody hybrydowej łączącej samoadaptacyjną ewolucję różnicową z algorytmem EM
    Autorzy:
    Kwedlo, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/88410.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    mieszaniny rozkładów normalnych
    ewolucja różnicowa
    algorytm EM
    grupowanie danych
    Gaussian mixture models
    differential evolution
    expectation maximization
    model-based clustering
    Opis:
    In the paper the problem of learning of Gaussian mixture models (GMMs) is considered. A new approach based on hybridization of a self-adaptive version of differential evolution (DE) with the classical EM algorithm is described. In this approach, called DEEM, the EM algorithm is run until convergence to fine-tune each solution obtained by the mutation and crossover operators of DE. To avoid the problem with parameter representation and infeasible solutions we use a method in which the covariance matrices are encoded using their Cholesky factorizations. In a simulation study GMMs were used to cluster synthetic datasets differing by a degree of separation between clusters. The results of experiments indicate that DE-EM outperforms the standard multiple restart expectation-maximization algorithm (MREM). For datasets with high number of features it also outperforms the state of-the-art random swap EM (RSEM).
    W pracy poruszono problem uczenia modeli mieszanin rozkładów normalnych. Zaproponowano nowe podejście, nazwane DE-EM, oparte na hybrydyzacji samoadaptacyjnego algorytmu ewolucji różnicowej i klasycznego algorytmu EM. W nowej metodzie rozwiązanie otrzymane jako wynik operatorów mutacji i krzyżowania jest poddawane optymalizacji lokalnej, prowadzonej aż do momentu uzyskania zbieżności, przez algorytm EM. Aby uniknąć problemu z reprezentacją macierzy kowariancji i niedopuszczalności rozwiązań użyto metody, w której macierze kowariancji są kodowane przy pomocy dekompozycji Cholesky’ego. W badaniach symulacyjnych modele mieszanin rozkładów normalnych zastosowano do grupowania danych syntetycznych. Wyniki eksperymentów wskazują, że metoda DE-EM osiąga lepsze wyniki niż standardowa technika wielokrotnego startu algorytmu ˙ EM. Dla zbiorów danych z dużą liczbą cech, metoda osiąga lepsze wyniki niż technika losowej wymiany rozwiązań połączona z algorytmem EM.
    Źródło:
    Advances in Computer Science Research; 2014, 11; 109-123
    2300-715X
    Pojawia się w:
    Advances in Computer Science Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ant-based clustering for flow graph mining
    Autorzy:
    Lewicki, Arkadiusz
    Pancerz, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/330782.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    possibly certain sequences
    flow graph
    rough set
    fuzzy set
    ant based clustering
    graf przepływu danych
    zbiór przybliżony
    zbiór rozmyty
    Opis:
    The paper is devoted to the problem of mining graph data. The goal of this process is to discover possibly certain sequences appearing in data. Both rough set flow graphs and fuzzy flow graphs are used to represent sequences of items originally arranged in tables representing information systems. Information systems are considered in the Pawlak sense, as knowledge representation systems. In the paper, an approach involving ant based clustering is proposed. We show that ant based clustering can be used not only for building possible large groups of similar objects, but also to build larger structures (in our case, sequences) of objects to obtain or preserve the desired properties.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 561-572
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Artykuł
    Tytuł:
    Performance Evaluation of a Zone-based Three-level Heterogeneous Clustering Protocol for WSNs
    Autorzy:
    Rao, Sanapala Shanmukha
    Shilpi
    Kumar, Arvind
    Powiązania:
    https://bibliotekanauki.pl/articles/24200745.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    clustering
    heterogeneous
    network lifetime
    stability period
    zone-based WSN
    Opis:
    This paper proposes a zone-based three-level heterogeneous clustering protocol (ZB-TLHCP) for heterogeneous WSNs. In ZB-TLHCP, the sensor field/region is divided into zones where super, advance, and normal nodes are deployed uniformly and randomly. The performance of the proposed ZB-TLHCP system is compared with that of zonal-stable election protocol (Z-SEP), distributed energy efficient clustering (DEEC), and threshold-based DEEC (TDEEC) protocol by varying the number of super and advance nodes, their energy levels for the fixed sensor field, and the total number of nodes. Matlab simulation results revealed that the proposed ZB-TLHCP solution performed better than Z-SEP, DEEC, and TDEEC protocols, as it increased the instability period, prolonged the network's lifetime, and achieved higher throughput values.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 3; 85--93
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Study of Wireless Sensor Networks to Comprehend their Relevance to Different Applications
    Autorzy:
    Agarkhed, Jayashree
    Dattatraya, Patil Yogita
    Patil, Siddarama
    Powiązania:
    https://bibliotekanauki.pl/articles/308904.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    clustering
    energy efficiency
    multipath-based routing
    wireless sensor network
    Opis:
    Wireless sensor networks (WSNs) have experienced enormous growth, both in terms of the technology used and their practical applications. In order to understand the features of WSNs that make the solution suitable for a specific purpose, one needs to be aware of the theoretical concepts behind and technological aspects of WSNs. In this paper, the significance of WSNs is illustrated, with a particular emphasis placed on their demands and on understanding researchrelated problems. A review of the literature available is presented as well. Detailed discussions concerning sensor node architecture, different types of sensors used and their relevance for various types of WSNs is presented, highlighting the need to achieve application-specific requirements without degrading service quality. Multipath and cluster-based routing protocols are compared in order to analyze QoS requirements they are capable of satisfying, and their suitability for different application areas is reviewed. This survey highlights the performance of different routing protocols, therefore providing guidelines enabling each of the routing techniques to be used, in an efficient manner, with factors such as specific network structure, protocol operation and routing path construction taken into consideration in order to achieve better performance.
    Źródło:
    Journal of Telecommunications and Information Technology; 2020, 2; 3-13
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Artykuł
    Tytuł:
    Method for the Player Profiling in the Turn-based Computer Games
    Autorzy:
    Bilski, Piotr
    Antoniuk, Izabella
    Łabędzki, Rafał
    Powiązania:
    https://bibliotekanauki.pl/articles/27311938.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    turn based games
    player profiling
    data clustering
    automated classification
    Opis:
    The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 3; 461--468
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Accuracy of generalized context patterns in the context based sequential patterns mining
    Autorzy:
    Ziembiński, R. Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/206061.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    knowledge discovery
    context based sequential pattern mining
    sequential context pattern clustering
    pattern accuracy
    Opis:
    A context pattern is a frequent subsequence mined from the context database containing set of sequences. This kind of sequential patterns and all elements inside them are described by additional sets of context attributes e.g. continuous ones. The contexts describe circumstances of transactions and sources of sequential data. These patterns can be mined by an algorithm for the context based sequential pattern mining. However, this can create large sets of patterns because all contexts related to patterns are taken from the database. The goal of the generalization method is to reduce the context pattern set by introducing a more compact and descriptive kind of patterns. This is achieved by finding clusters of similar context patterns in the mined set and transforming them to a smaller set of generalized context patterns. This process has to retain as much as possible information from the mined context patterns. This paper introduces a definition of the generalized context pattern and the related algorithm. Results from the generalization may differ as depending on the algorithm design and settings. Hence, generalized patterns may reflect frequent information from the context database differently. Thus, an accuracy measure is also proposed to evaluate the generalized patterns. This measure is used in the experiments presented. The generalized context patterns are compared to patterns mined by the basic sequential patterns mining with prediscretization of context values.
    Źródło:
    Control and Cybernetics; 2011, 40, 3; 585-603
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A density-based method for the identification of disjoint and non-disjoint clusters with arbitrary and non-spherical shapes
    Autorzy:
    Ben Ncir, Chiheb-Eddine
    Powiązania:
    https://bibliotekanauki.pl/articles/2097971.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    overlapping clustering
    non-disjoint clusters
    density-based methods
    clusters with non-spherical shapes
    Opis:
    The ability of clustering methods to build both disjoint and non-disjoint partitionings of data has become an important issue in unsupervised learning. Although this problem has been studied during the last decades resulting in several proposed overlapping clustering methods in the literature, most of existing methods fail to look for clusters having arbitrary and non-spherical shapes. In addition, most of these existing methods require to pre-configure the number of clusters in prior, which is not a trivial task in real life application of clustering. To solve all these issues, we propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect both disjoint and non-disjoint partitioning even when boundaries between clusters have complex separations with arbitrary forms and shapes. The proposed method is based on density and distances to detect highly dense regions and connected groups in data without the necessity to pre-configure the number of clusters. Experiments performed on artificial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.
    Źródło:
    Computer Science; 2021, 22 (2); 169-190
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extraction of Polish noun senses from large corpora by means of clustering
    Autorzy:
    Broda, B.
    Piasecki, M.
    Szpakowicz, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/969804.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    corpus linguistics
    semantic similarity
    Polish nouns
    word clustering
    Clustering by Committee
    co-occurrence retrieval models
    rank weight function
    Polish WordNet
    WordNet-based synonymy test
    document clustering
    keywords extraction
    Opis:
    We investigate two methods of identifying noun senses, based on clustering of lemmas and of documents. We have adapted to Polish the well-known algorithm of Clustering by Committee, and tested it on very large Polish corpora. The evaluation by means of a WordNet-based synonymy test used Polish wordnet (plWordNet 1.0). Various clustering algorithms were analysed for the needs of extraction of document clusters as indicators of the senses of words which occur in them. The two approaches to wordsense identification have been compared, and conclusions drawn.
    Źródło:
    Control and Cybernetics; 2010, 39, 2; 401-420
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of data aggregation methods and related issues in Wireless Sensor Networks
    Autorzy:
    Nels, S. Ninisha
    Singh, J. Amar Pratap
    Powiązania:
    https://bibliotekanauki.pl/articles/2050167.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    data aggregation
    wireless sensor network
    WSN
    clustering
    cluster head
    CH
    cluster-based data aggregation
    Opis:
    Data aggregation is the process aimed at reducing the transmission count of packets being transmitted in the framework of in-network data processing. It is the data transmission model that takes the information transmitted from different nodes and generates a single data packet after finding and eliminating the redundant packets. Accordingly, this process decreases the transmission count and makes it possible to consume less energy. The major issues in data aggregation mechanism are related to reduction of latency and to energy balancing. Moreover, it is very complex to resolve the issue of packet loss, which is the failure of one or more transmitted packets to arrive at their destination due to the bad and/or congested channel conditions. The present survey involves a collection of 50 research papers dealing with the data aggregation models in wireless sensor networks (WSN). Various data aggregation methods, like the cluster-based approach, structure-free method, tree-based approach, in-network methods, and energy based aggregation model are considered in this survey, regarding the application and the energy usage involved. On the basis of the survey, the issues and drawbacks faced by the respective methodologies are highlighted. In addition, the paper presents simple statistics of the studies considered with respect to the performance measures, simulation tools, publication year, and classification of methods. The future dimensions of the respective research are supposed to be based on the challenges identified in this survey.
    Źródło:
    Control and Cybernetics; 2020, 49, 4; 419-446
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new approach for the clustering using pairs of prototypes
    Autorzy:
    Jezewski, M.
    Czabanski, R.
    Leski, J.
    Horoba, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/333693.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    fuzzy clustering
    pairs of prototypes
    fuzzy rule-based classification
    grupowanie rozmyte
    pary prototypów
    rozmyta klasyfikacja oparta na regułach
    Opis:
    In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 113-121
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Novel visual object descriptor using surf and clustering algorithms
    Autorzy:
    Grycuk, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/122762.pdf
    Data publikacji:
    2016
    Wydawca:
    Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
    Tematy:
    k-means
    mean shift
    clustering
    image description
    SURF
    keypoints
    content-based image retrieval (CBIR)
    opis obrazu
    algorytmy grupowania
    detekcja punktów kluczowych
    Opis:
    In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage.
    Źródło:
    Journal of Applied Mathematics and Computational Mechanics; 2016, 15, 3; 37-46
    2299-9965
    Pojawia się w:
    Journal of Applied Mathematics and Computational Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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