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.


pre-clustering algorithm; internal validity measures

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Published : 2017-09-30

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.

Volodymyr Mosorov
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