DocumentCode
1811797
Title
A linguistic feature based text clustering method
Author
Shi, Kansheng ; Li, Lemin ; He, Jie ; Liu, Haitao ; Zhang, Naitong ; Song, Wentao
Author_Institution
Shanghai Jiaotong Univ., Shanghai, China
fYear
2011
fDate
15-17 Sept. 2011
Firstpage
108
Lastpage
112
Abstract
The traditional K-means algorithm is sensitive to the initial point, easy to fall into local optimum. In order to avoid this kind of flaw, an improved K-means text clustering method WIKTCM is proposed. The new method creates an innovative initial centers selection method and accommodates the contribution of characteristics of different parts of speech to the text. In addition, the impact of outliers is considered. Experimental results show that the new method has better clustering results.
Keywords
computational linguistics; pattern clustering; text analysis; WIKTCM; linguistic feature; text clustering; traditional K-means algorithm; Algorithm design and analysis; Clustering algorithms; Clustering methods; Computers; Educational institutions; Mathematical model; Speech; K-means; Sample average similarity; Text clustering; VSM;
fLanguage
English
Publisher
ieee
Conference_Titel
Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-61284-203-5
Type
conf
DOI
10.1109/CCIS.2011.6045042
Filename
6045042
Link To Document