A k-populations algorithm for clustering categorical data

doi:10.1016/j.patcog.2004.11.017

A k-populations algorithm for clustering categorical data

2005-07-01

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DOI：10.1016/j.patcog.2004.11.017

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In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keyword:

clustering

categorical data

hierarchical algorithm

k-modes algorithm

fuzzy k-modes algorithm

A k-populations algorithm for clustering categorical data

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