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

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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

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