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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