K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset
Zengyou He, Xiaofei Xu, Shengchun Deng, Bin Dong · arXiv (Cornell University) · 2005
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.