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Attribute Value Weighting in K-Modes Clustering

Zengyou He·Xaiofei Xu·Shengchun Deng
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Abstract

In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.

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Zengyou He, Xaiofei Xu, Shengchun Deng. 2007-01-03. Attribute Value Weighting in K-Modes Clustering. https://arxiv.org/abs/cs/0701013

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