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ID 14816
Eprint ID
14816
FullText URL
Author
Yamaguchi Takashi
Ichimura Takumi
Kenneth J. Mackin
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.
Published Date
2008-12-11
Publication Title
Proceedings : Fourth International Workshop on Computational Intelligence & Applications
Volume
volume2008
Issue
issue1
Publisher
IEEE SMC Hiroshima Chapter
Start Page
139
End Page
144
ISSN
1883-3977
Content Type
Conference Paper
language
英語
Event Title
Fourth International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2008
Event Location
東広島市
Event Location Alternative
Higashi-Hiroshima City
File Version
publisher
Refereed
True
Eprints Journal Name
IWCIA