ID | 14816 |
Eprint ID | 14816
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FullText URL | |
Author |
Yamaguchi Takashi
Ichimura Takumi
Kenneth J. Mackin
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Abstract | It is known that the classification of medical data is
difficult problem because the medical data has ambiguous
information or missing data. As a result, the classification method that can handle ambiguous information or missing data is necessity. In this paper we proposed an adaptive tree structure clustering method in order to clarify clustering result of selforganizing feature maps. For the evaluating effectiveness of proposed clustering method for the data set with ambiguous information, we applied an adaptive tree structured clustering
method for classification of coronary heart disease database.
Through the computer simulation we showed that the proposed
clustering method was effective for the ambiguous data set.
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Published Date | 2008-12-11
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Publication Title |
Proceedings : Fourth International Workshop on Computational Intelligence & Applications
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Volume | volume2008
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Issue | issue1
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Publisher | IEEE SMC Hiroshima Chapter
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Start Page | 139
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End Page | 144
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ISSN | 1883-3977
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Content Type |
Conference Paper
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language |
English
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Event Title | Fourth International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2008
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Event Location | 東広島市
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Event Location Alternative | Higashi-Hiroshima City
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File Version | publisher
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Refereed |
True
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Eprints Journal Name | IWCIA
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