Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Squeezer: an efficient algorithm for clustering categorical data
Journal of Computer Science and Technology
Discovering cluster-based local outliers
Pattern Recognition Letters
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Approximation algorithms for k-modes clustering
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Attribute value weighting in k-modes clustering
Expert Systems with Applications: An International Journal
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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.