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ID 25635
file
creator
Yamaguchi, Takashi
Ichimura, Takumi
Mackin, Kenneth J.
NDC
Technology. Engineering
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.
journal title
Fourth International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2008
start page
139
end page
144
date of issued
2008-12
publisher
IEEE SMC Hiroshima Chapter
issn
1883-3977
language
eng
nii type
Conference Paper
HU type
Conference Papers
DCMI type
text
format
application/pdf
text version
publisher
rights
(c) Copyright by IEEE SMC Hiroshima Chapter.
department
Graduate School of Engineering