Fuzzy clustering for documents based on optimization of classifier using the genetic algorithm

被引:0
|
作者
Youn, JI [1 ]
Eun, HJ [1 ]
Kim, YS [1 ]
机构
[1] Chonbuk Natl Univ, Div Elect & Informat Engn, Duckjin Gu Jeonju, South Korea
关键词
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
It is a problem that established document categorization method reflects the semantic relation inaccurately at feature expression of document. For the purpose of solving this problem, we propose a genetic algorithm and C-Means clustering algorithm for choosing an appropriate set of fuzzy clustering for classification problems of documents. The aim of the proposed method is to find a minimum set of fuzzy cluster that can correctly classify all training documents. The number of fuzzy pseudo-partition and the shapes of the fuzzy membership functions that we use the classification criteria are determined by the genetic algorithms. Then, the classifier decides using fuzzy c-means clustering algorithms for documents classification. A solution obtained by the genetic algorithm is a set of fuzzy clustering, and its fitness function is determined by fuzzy membership function.
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页码:10 / 20
页数:11
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