Presentation 2004/11/28
Clustering Time-series Data Based on the Modified Multiscale Matching Technique(Medical Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
SHOJI HIRANO, SHUSAKU TSUMOTO,
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Abstract(in English) This paper presents an improved version of time-series multiscale matching method that eludes the problem of shrinkage. The key idea is the development of hew segment representation. The shape parameters of a segment at high scale are now directly obtained using the shape parameters of base segments at the lowest scale, instead of using shapes represented by multiscale description. Multiscale shapes are now used only to obtain the hierarchy of the segments; since segment parameters are obtained independently of multiscale shapes, shrinkage does not distort them. We examined the usefulness of the method on the cylinder-bell-funnel dataset. The results demonstrated that the dissimilarity matrix produced by the proposed method, combined with conventional clustering techniques, lead to the successful clustering.
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Paper # AI2004-44
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Committee AI
Conference Date 2004/11/28(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Clustering Time-series Data Based on the Modified Multiscale Matching Technique(Medical Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
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1st Author's Name SHOJI HIRANO
1st Author's Affiliation Department of Medical Informatics, Shimane University, School of Medicine()
2nd Author's Name SHUSAKU TSUMOTO
2nd Author's Affiliation Department of Medical Informatics, Shimane University, School of Medicine
Date 2004/11/28
Paper # AI2004-44
Volume (vol) vol.104
Number (no) 486
Page pp.pp.-
#Pages 6
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