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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)

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.

Keywords

Equivalence Relation Cluster Method Cluster Result Good Cluster Dissimilarity Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Berkhin, P.: Survey of clustering data mining techniques. Accrue Software Research Paper (2002), http://www.accrue.com/products/researchpapers.html
  2. 2.
    Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Arnold Publishers (2001)Google Scholar
  3. 3.
    Hirano, S., Tsumoto, S.: An indiscernibility-based clustering method with iterative refinement of equivalence relations. Journal of Advanced Computational Intelligence and Intelligent Informatics 7, 169–177 (2003)Google Scholar
  4. 4.
    Neyman, J., Scott, E.L.: Statistical approach to problems of cosmology. Journal of the Royal Statistical Society, Series B20, 1–43 (1958)MathSciNetGoogle Scholar
  5. 5.
  6. 6.
    Hirano, S., Tsumoto, S.: Clustering time-series medical databases based on the improved multiscale matching. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 612–621. Springer, Heidelberg (2005)CrossRefGoogle Scholar

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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