A nonnegative matrix factorization framework for semi-supervised document clustering with dual constraints-Reference-Cited by-同舟云学术

A nonnegative matrix factorization framework for semi-supervised document clustering with dual constraints

Published:2012-10-13 Issue:3 Volume:36 Page:629-651
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Ma Huifang,Zhao Weizhong,Shi Zhongzhi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10115-012-0560-3.pdf

Reference48 articles.

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3. Basu S, Bilenko M et al (2004) A probabilistic framework for semi-supervised clustering. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 59–68

4. Beil F, Ester M et al (2002) Frequent term-based text clustering. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, pp 436–442

5. Berry MW, Browne M et al (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52:155–173

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