Symbolic Hierarchical Clustering for Pain Vector

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

研究成果: Chapter

抄録

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.

本文言語English
ホスト出版物のタイトルIntelligent Decision Technologies Proceedings of the 4th International Conference on Intelligent Decision
編集者Jain Lakhmi, Howlett Robert, Watada Junzo, Watanabe Toyohide, Gloria Phillips-Wren
ページ117-124
ページ数8
DOI
出版ステータスPublished - 2012 12 1

出版物シリーズ

名前Smart Innovation, Systems and Technologies
16
ISSN(印刷版)2190-3018
ISSN(電子版)2190-3026

ASJC Scopus subject areas

  • 決定科学(全般)
  • コンピュータ サイエンス(全般)

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