DocumentCode
2463420
Title
Research on the Improvement of the Fuzzy C-Means Text Mining Methods Based on Genetic Algorithm
Author
Li, Xiang-dong ; Fu, Zhi-hua ; Wu, Li-ping ; Liu, Xiao-bin ; Tan, Run-hua
Author_Institution
Sch. of Manage., Hebei Univ. of Technol., Tianjin, China
Volume
3
fYear
2010
fDate
16-17 Dec. 2010
Firstpage
13
Lastpage
16
Abstract
The paper adopts the fuzzy c-means text mining method in lots of text mining methods. But aim at the defect that the initial value of the fuzzy c-means is more sensitivity and poor stability, an improved GAFCM text mining method has been put forward. GAFCM uses global search features of genetic algorithms to improve the fuzzy c-means. Finally, it has proved that the improved text mining method has boosted in term of both accuracy and stability by an example.
Keywords
data mining; fuzzy set theory; genetic algorithms; pattern clustering; text analysis; fuzzy c-means clustering text mining methods; genetic algorithm; global search features; stability; Accuracy; Artificial neural networks; Biological cells; Clustering algorithms; Computers; Education; Text mining; fuzzy c-means; genetic algorithm; matrix; text clustered mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-9247-3
Type
conf
DOI
10.1109/GCIS.2010.269
Filename
5709312
Link To Document