ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS
Journal Title: Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska - Year 2017, Vol 7, Issue 3
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.
Authors and Affiliations
Volodymyr Mosorov, Taras Panskyi, Sebastian Biedron
Keywords
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- EP ID EP227006
- DOI 10.5604/01.3001.0010.5216
- Views 109
- Downloads 0
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