ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS
Volodymyr Mosorov
w.mosorow@kis.p.lodz.plLodz University of Technology, Institute of Applied Computer Science (Poland)
Taras Panskyi
Lodz University of Technology, Institute of Applied Computer Science (Poland)
Sebastian Biedron
Lodz University of Technology, Institute of Applied Computer Science (Poland)
Abstract
In this paper the analysis of k-specified (namely k-means) crisp data partitioning pre-clustering algorithm’s termination criterion performance is described. The results have been analyzed using the clustering validity indices. Termination criterion allows analyzing data with any number of clusters. Moreover, introduced criterion in contrast to the known validity indices enables to analyze data that make up one cluster.
Keywords:
pre-clustering algorithm, internal validity measuresReferences
Charrad M., Ghazzali N., Boiteau V., Niknafs A.: NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistic Software, 61(6), 2014, 1–36.
Google Scholar
Davies D.L., Bouldin D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1, no. 2, 1979, 224–227.
Google Scholar
Desgraupes B.: Clustering indices. University Paris Ouest, Lab Modal’X, 2013.
Google Scholar
Dunn J.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4, 1974, 95–104.
Google Scholar
Fraley C., Raftery A.E.: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 1998, 578–588.
Google Scholar
Gini, C.: Variabilitа e mutabilitа Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi, 1912, Bologna: Tipogr. Di P. Cuppini.
Google Scholar
Halkidi M., Batistakis Y., Vazirgiannis M.: On clustering validation techniques. J. Intell. Inf. Syst., 17(2-3), 2001, 107–145.
Google Scholar
Jung Y., Park H., Du D-Z., Drake B.L.: A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering. Journal of Global Optimization, 25(1), 2003, 91–111
Google Scholar
Ketchen Jr. Dj, Shook Cl.: The Application Of Cluster Analysis In Strategic Management Research: An Analysis And Critique, Strategic Management Journal, 17(6), 1996, 441–458.
Google Scholar
McCallum A., Nigam K., Ungar L.H.: Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching, Sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000.
Google Scholar
Mosorov V., Panskyi T., Biedron S.: Development of a stopping rule of clustering performance by using the connected acyclic graph. Eastern-European Journal of Enterprise Technologies, 5, 9(77), 2015, 24–30.
Google Scholar
Mosorov V., Tomczak L.: Image Texture Defect Detection Method UsingFuzzy C-Means Clustering for Visual Inspection Systems. Arabian Journal for Science and Engineering, 39(4), 2014, 3013–3022.
Google Scholar
RapidMiner GmbH: Cluster distance performance – RapidMiner documentation. http://docs.rapidminer.com/studio/operators/validation/performance/segmentation/cluster_distance_performance.html
Google Scholar
Rousseeuw P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 1987, 53–65.
Google Scholar
Sheikholeslami C., Chatterjee S., Zhang A.: WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Database. The International Journal on Very Large Data Bases, 8(3-4), 2000, 289–304.
Google Scholar
Theodoridis S., Koutroubas K.: Pattern Recognition 4th Edition, Academic Press, 2008.
Google Scholar
Authors
Volodymyr Mosorovw.mosorow@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland
Authors
Taras PanskyiLodz University of Technology, Institute of Applied Computer Science Poland
Authors
Sebastian BiedronLodz University of Technology, Institute of Applied Computer Science Poland
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