Improving K-Modes Algorithm Considering Frequencies of Attribute Values in Mode

In this paper, we present an experimental study on applying a new dissimilarity measure to the k-modes clustering algorithm to improve its clustering accuracy. The measure is based on the idea that the similarity between a data object and cluster mode, is directly proportional to the sum of relative frequencies of the common values in mode. Experimental results on real life datasets show that, the modified algorithm is superior to the original k-modes algorithm with respect to clustering accuracy.

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