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K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data

Zengyou He·Xiaofei Xu·Shengchun Deng
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Abstract

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular k-means algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, Average Normalized Mutual Information-ANMI) borrowed from cluster ensemble. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-art categorical data clustering algorithms with respect to clustering accuracy.

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Zengyou He, Xiaofei Xu, Shengchun Deng. 2005-11-03. K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data. https://arxiv.org/abs/cs/0511013

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