  Clustering categorical data is an integral part of data mining and has
attracted much attention recently. In this paper, we present k-histogram, a new
efficient algorithm for clustering categorical data. The k-histogram algorithm
extends the k-means algorithm to categorical domain by replacing the means of
clusters with histograms, and dynamically updates histograms in the clustering
process. Experimental results on real datasets show that k-histogram algorithm
can produce better clustering results than k-modes algorithm, the one related
with our work most closely.
