On Constructing Clusters from Non-Euclidean Dissimilarity Matrix by Using Rough Clustering

  • Shoji Hirano
  • Shusaku Tsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4012)


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.


Equivalence Relation Cluster Method Cluster Result Good Cluster Dissimilarity Matrix 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shoji Hirano
    • 1
  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane University School of MedicineShimaneJapan

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