Attribute Value Weighting in K-Modes Clustering

TL;DR

This paper enhances the k-modes clustering algorithm by incorporating attribute value weighting, leading to improved clustering accuracy through stronger intra-cluster similarities, as demonstrated on real datasets.

Contribution

It introduces a novel attribute value weighting technique into k-modes clustering, improving cluster quality and accuracy over the standard method.

Findings

Weighted k-modes outperforms standard k-modes in accuracy

Enhanced intra-cluster similarity achieved

Effective on real-world datasets

Abstract

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.