  In this paper, the traditional k-modes clustering algorithm is extended by
weighting attribute value matches in dissimilarity computation. The use of
attribute value weighting technique makes it possible to generate clusters with
stronger intra-similarities, and therefore achieve better clustering
performance. Experimental results on real life datasets show that these value
weighting based k-modes algorithms are superior to the standard k-modes
algorithm with respect to clustering accuracy.
