Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure

Authors:
S. Aranganayagi;K. Thangavel
Affiliations:
-;-
Venue:
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Year:
2007

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Abstract

Cluster analysis is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. Clustering categorical data is an important research area data mining. In this paper we propose a novel algorithm to cluster categorical data. Based on the minimum dissimilarity value objects are grouped into cluster. In the merging process, the objects are relocated using silhouette coefficient. Experimental results show that the proposed method is efficient. Keywords: Data mining, clustering, categorical data, silhouette coefficient.