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	Text clustering based on asymmetric similarity
	Song Shaoxu ; Li Chunping
	2010-05-07 ; 2010-05-07
关键词	Practical Theoretical or Mathematical/ learning (artificial intelligence) pattern clustering sparse matrices text analysis/ text clustering method distance-based dissimilarity method asymmetric similarity approach sparse matrix hierarchical clustering/ C6130D Document processing techniques C1230L Learning in AI C1110 Algebra C1250 Pattern recognition
中文摘要	Text clustering data sets have sparse data spaces, with existing text clustering methods using distance-based dissimilarity to measure the document similarity. The document discrimination ability can be strengthened by a asymmetric similarity approach for text clustering. The asymmetric similarity is measured by a clustering analysis of the strong components of the sparse matrix. The approach provides a conceptual structure after the hierarchical clustering. Tests on textual data sets show that the asymmetric similarity measure provides higher precision with less run time than the distance-based dissimilarity method. With small numbers of clusters, the accuracy is improved by about 20%.
语种	中文 ; 中文
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/16752]
专题	清华大学
推荐引用方式 GB/T 7714	Song Shaoxu,Li Chunping. Text clustering based on asymmetric similarity[J],2010, 2010.
APA	Song Shaoxu,&Li Chunping.(2010).Text clustering based on asymmetric similarity..
MLA	Song Shaoxu,et al."Text clustering based on asymmetric similarity".(2010).

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