On constructing clusters from non-euclidean dissimilarity matrix by using rough clustering

Authors:
Shoji Hirano;Shusaku Tsumoto
Affiliations:
Department of Medical Informatics, Shimane University School of Medicine, Shimane, Japan;Department of Medical Informatics, Shimane University School of Medicine, Shimane, Japan
Venue:
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Year:
2006

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Abstract

In this paper, we present a clustering method which can construct interpretable clusters from a dissimilarity matrix containing relatively or subjectively defined dissimilarities. Experimental results on the synthetic, numerical datasets demonstrated that this method could produce good clusters even when the proximity of the objects did satisfy the triangular inequality. Results on chronic hepatitis dataset also demonstrated that this method could absorb local disturbance in the proximity matrix and produce interpretable clusters containing time series that have similar patterns.