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A New Similarity Measure for Categorical Attribute-Based Clustering  

Kim, Min (한국과학기술연구원 인지로봇센터)
Jeon, Joo-Hyuk (한국과학기술원 전산학과)
Woo, Kyung-Gu (삼성전자 종합기술원 SW 선행연구소)
Kim, Myoung-Ho (한국과학기술원 전산학과)
Publication Information
Journal of KIISE:Databases / v.37, no.2, 2010 , pp. 71-81 More about this Journal
Abstract
The problem of finding clusters is widely used in numerous applications, such as pattern recognition, image analysis, market analysis. The important factors that decide cluster quality are the similarity measure and the number of attributes. Similarity measures should be defined with respect to the data types. Existing similarity measures are well applicable to numerical attribute values. However, those measures do not work well when the data is described by categorical attributes, that is, when no inherent similarity measure between values. In high dimensional spaces, conventional clustering algorithms tend to break down because of sparsity of data points. To overcome this difficulty, a subspace clustering approach has been proposed. It is based on the observation that different clusters may exist in different subspaces. In this paper, we propose a new similarity measure for clustering of high dimensional categorical data. The measure is defined based on the fact that a good clustering is one where each cluster should have certain information that can distinguish it with other clusters. We also try to capture on the attribute dependencies. This study is meaningful because there has been no method to use both of them. Experimental results on real datasets show clusters obtained by our proposed similarity measure are good enough with respect to clustering accuracy.
Keywords
clustering; similarity measure; k-means clustering;
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