Symbolic Hierarchical Clustering for Pain Vector

Kotoe Katayama, Rui Yamaguchi, Seiya Imoto, Keiko Matsuura, Kenji Watanabe, Satoru Miyano

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies Proceedings of the 4th International Conference on Intelligent Decision
EditorsJain Lakhmi, Howlett Robert, Watada Junzo, Watanabe Toyohide, Gloria Phillips-Wren
Number of pages8
Publication statusPublished - 2012
Externally publishedYes

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


  • Distribution-Valued Data
  • Visual Analogue Scale

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)


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