Presentation 2007-09-04
Cluster Analysis for Identifying the Hierarchical Structure of Subpopulations in Mixture Models
Kazunori IWATA, Akira HAYASHI,
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Abstract(in English) This paper provides analysis of clusters of labeled samples to identify their underlying hierarchical structure. The key in this identification is to select a suitable measure of dissimilarity among clusters characterized by subpopulations of the samples. Accordingly, we introduce a dissimilarity measure suitable for measuring a hierarchical structure of subpopulations that fit the mixture model. Glass identification is used as a practical problem for hierarchical cluster analysis, in the computational experiments in this paper. In the experimental results, we exhibit the effectiveness of the introduced measure, compared to several others.
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Keyword(in English) hierarchical cluster analysis / mixture models / dissimilarity measure / glass identification
Paper # PRMU2007-86,HIP2007-95
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Committee HIP
Conference Date 2007/8/27(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Cluster Analysis for Identifying the Hierarchical Structure of Subpopulations in Mixture Models
Sub Title (in English)
Keyword(1) hierarchical cluster analysis
Keyword(2) mixture models
Keyword(3) dissimilarity measure
Keyword(4) glass identification
1st Author's Name Kazunori IWATA
1st Author's Affiliation Graduate School of Information Sciences, Hiroshima City University()
2nd Author's Name Akira HAYASHI
2nd Author's Affiliation Graduate School of Information Sciences, Hiroshima City University
Date 2007-09-04
Paper # PRMU2007-86,HIP2007-95
Volume (vol) vol.107
Number (no) 207
Page pp.pp.-
#Pages 6
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