Attribute Value Weighting in K-Modes Clustering

He, Zengyou; Xu, Xaiofei; Deng, Shengchun

Attribute Value Weighting in K-Modes Clustering

Authors

He, Zengyou

Xu, Xaiofei

Deng, Shengchun

Description

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.
15 pages