Symbolic Hierarchical Clustering for Pain Vector

  • Kotoe Katayama
  • Rui Yamaguchi
  • Seiya Imoto
  • Keiko Matsuura
  • Kenji Watanabe
  • Satoru Miyano
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 16)

Abstract

We propose a hierarchical clustering in the framework of Symbolic Data Analysis(SDA). SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also “higher-level units”; sets, intervals, distributions, etc. Most SDA works have dealt with only intervals as the descriptions. We already proposed “pain distribution” as new type data in SDA. In this paper, we define new “pain vector” as new type data in SDA and propose a hierarchical clustering for this new type data.

Keywords

Visual Analogue Scale Distribution-Valued Data 

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References

  1. 1.
    Billard, L., Diday, E.: Symbolic Data Analysis. Wiley, NewYork (2006)MATHCrossRefGoogle Scholar
  2. 2.
    Bock, H.-H., Diday, E.: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Berlin (2000)Google Scholar
  3. 3.
    Diday, E.: The symbolic approach in clustering and related methods of Data Analysis. In: Bock, H. (ed.) Proc. IFCS Classification and Related Methods of Data Analysis, Aachen, Germany. North-Holland (1988)Google Scholar
  4. 4.
    Diday, E.: The symbolic approach in clustering and related methods of Data Analysis. In: Bock, H. (ed.) Classification and Related methods of Data Analysis, pp. 673–684. North-Holland, Amsterdam (1988)Google Scholar
  5. 5.
    Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures. In: Proceedings of CVPR, pp. 487–494 (2006)Google Scholar
  6. 6.
    Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recognition 24(6), 567–578 (1991)CrossRefGoogle Scholar
  7. 7.
    Katayama, K., Suzukawa, A., Minami, H., Mizuta, M.: Linearly Restricted Principal Components in k Groups. In: Electronic proceedings of Knowledge Extraction and Modeling, Villa Orlandi, Island of Capri, Italy (2006)Google Scholar
  8. 8.
    Katayama, K., Yamaguchi, R., Imoto, S., Matsuura, K., Watanabe, K., Miyano, S.: Clustering for Visual Analogue Scale Data in Symbolic Data Analysis. Procedia Computer Science 6, 370–374 (2011)CrossRefGoogle Scholar
  9. 9.
    Kullback, S.: Information theory and statisticsh. Dover Publications, New York (1968)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kotoe Katayama
    • 1
  • Rui Yamaguchi
    • 1
  • Seiya Imoto
    • 1
  • Keiko Matsuura
    • 2
  • Kenji Watanabe
    • 2
  • Satoru Miyano
    • 1
  1. 1.Human Genome Center, Institute of Medical ScienceThe University of TokyoMinato-kuJapan
  2. 2.Center for Kampo MedicineKeio University School of MedicineShinjuku-kuJapan

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