  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.
