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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | hierarchical cluster analysis / mixture models / dissimilarity measure / glass identification |
Paper # | PRMU2007-86,HIP2007-95 |
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Committee | HIP |
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Conference Date | 2007/8/27(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Human Information Processing (HIP) |
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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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