Paper Info
Title
Fuzzy clustering for documents based on optimization of classifier using the genetic algorithm
Abstract
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.
Year
DOI
Venue
2005
10.1007/11424826_2
ICCSA
Keywords
Field
DocType
fuzzy pseudo-partition,fuzzy clustering,fuzzy cluster,genetic algorithm,classification criterion,fuzzy membership function,appropriate set,classification problem,fuzzy c-means,documents classification,fitness function
Fuzzy clustering,Data mining,Defuzzification,Fuzzy classification,Correlation clustering,Computer science,Fuzzy set operations,Fuzzy set,FLAME clustering,Membership function
Conference
Volume
ISSN
ISBN
3481
0302-9743
3-540-25861-2
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Ju-In Youn100.68
He-Jue Eun211.06
Yong-sung Kim331028.97