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
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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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