Text document clustering with metric learning

Wang, Jinlong, Wu, Shunyao, Vu, Huy Quan and Li, Gang 2010, Text document clustering with metric learning, in Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, N.Y., pp. 783-784.

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Title Text document clustering with metric learning
Author(s) Wang, Jinlong
Wu, Shunyao
Vu, Huy Quan
Li, Gang
Conference name Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (33rd : 2010 : Geneva, Switzerland)
Conference location Geneva, Switzerland
Conference dates 19-23 July 2010
Title of proceedings Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Editor(s) Chen, Hsin-Hsi
Efthimiadis, Efthimis N.
Savoy, Jacques
Crestani, Fabio
Marchand-Maillet, Stephane
Publication date 2010
Conference series Association for Computing Machinery Special Interest Group on Information Retrieval International Conference
Start page 783
End page 784
Total pages 2
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) document clustering
metric learning
Summary One reason for semi-supervised clustering fail to deliver satisfactory performance in document clustering is that the transformed optimization problem could have many candidate solutions, but existing methods provide no mechanism to select a suitable one from all those candidates. This paper alleviates this problem by posing the same task as a soft-constrained optimization problem, and introduces the salient degree measure as an information guide to control the searching of an optimal solution. Experimental results show the effectiveness of the proposed method in the improvement of the performance, especially when the amount of priori domain knowledge is limited.
ISBN 9781605588964
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810107 National Security
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, by the Association for Computing Machinery, Inc.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034433

Document type: Conference Paper
Collection: School of Information Technology
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