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Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure | IEEE Conference Publication | IEEE Xplore

Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure


Abstract:

Cluster analysis is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. Clustering categorical data is an important re...Show More

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.
Date of Conference: 13-15 December 2007
Date Added to IEEE Xplore: 07 January 2008
Print ISBN:0-7695-3050-8
Conference Location: Sivakasi, India

References

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