University of Hertfordshire

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Documents

  • Renato Cordeiro De Amorim
  • Vladimir Makarenkov
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Original languageEnglish
Pages (from-to)700-707
JournalNeurocomputing
Journal publication date15 Jan 2016
Volume173
Issue3
Early online date17 Aug 2015
DOIs
Publication statusPublished - 15 Jan 2016

Abstract

We consider the Weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric in a way that feature weights become feature re-scaling factors for any considered exponent. Moreover, the traditional Silhouette clustering validity index was adapted to deal with both numerical and categorical types of features. Finally, we show that our new method usually outperforms traditional K-Means as well as the recently proposed WK-DC clustering algorithm.

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