Clustering using difference criterion of distortion ratios

Authors:
Fujiki Morii
Affiliations:
Department of Information and Computer Sciences, Nara Women's University, Nara, Japan
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

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Abstract

Clustering using a difference criterion of distortion-ratios on clusters is investigated for data sets with large statistical differences of class data, where K-Means algorithm (KMA) and Learning Vector Quantization (LVQ) cannot necessarily reveal the good performance. After obtaining cluster centers by KMA or LVQ, a split and merge procedure with the difference criterion is executed. Focusing on an interesting data set which is not resolved by KMA or LVQ, some experimental clustering results based on the difference criterion and the split and merge procedure are provided.