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 kmeans 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. This algorithm is easy to implement, requiring multiple hash tables as the only major data structure. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-the-art categorical data clustering algorithms with respect to clustering accuracy.
ECQ is equivalent to the mutual information based clustering quality index REF .
k-ANMI : A mutual information based clustering algorithm for categorical data
ArXiv
ArXiv
Computer Science