Attribute value weighting in k-modes clustering

In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.