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

He, Zengyou; Xu, Xiaofei; Deng, Shengchun

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

Authors

He, Zengyou

Xu, Xiaofei

Deng, Shengchun

Description

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.
18 pages