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Text document clustering with metric learning
conference contribution
posted on 2010-01-01, 00:00 authored by J Wang, S Wu, Huy Quan Vu, Gang LiGang LiOne 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.
History
Event
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (33rd : 2010 : Geneva, Switzerland)Pagination
783 - 784Publisher
Association for Computing MachineryLocation
Geneva, SwitzerlandPlace of publication
New York, N.Y.Start date
2010-07-19End date
2010-07-23ISBN-13
9781605588964Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2010, by the Association for Computing Machinery, Inc.Editor/Contributor(s)
H Chen, E Efthimiadis, J Savoy, F Crestani, S Marchand-MailletTitle of proceedings
Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information RetrievalUsage metrics
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