ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS

Volodymyr Mosorov

w.mosorow@kis.p.lodz.pl
Lodz 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 measures

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

Download


Published
2017-09-30

Cited by

Mosorov, V. ., Panskyi, T. ., & Biedron, S. . (2017). ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(3), 56–59. https://doi.org/10.5604/01.3001.0010.5216

Authors

Volodymyr Mosorov 
w.mosorow@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Taras Panskyi 

Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Sebastian Biedron 

Lodz University of Technology, Institute of Applied Computer Science Poland

Statistics

Abstract views: 237
PDF downloads: 63


Most read articles by the same author(s)

1 2 > >>