Text Clustering with Limited User Feedback Under Local Metric Learning

  • Ruizhang Huang
  • Zhigang Zhang
  • Wai Lam
Conference paper

DOI: 10.1007/11880592_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)
Cite this paper as:
Huang R., Zhang Z., Lam W. (2006) Text Clustering with Limited User Feedback Under Local Metric Learning. In: Ng H.T., Leong MK., Kan MY., Ji D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg

Abstract

This paper investigates the idea of incorporating incremental user feedbacks and a small amount of sample documents for some, not necessarily all, clusters into text clustering. For the modeling of each cluster, we make use of a local weight metric to reflect the importance of the features for a particular cluster. The local weight metric is learned using both the unlabeled data and the constraints generated automatically from user feedbacks and sample documents. The quality of local metric is improved by incorporating more precise constraints. Improving the quality of local metric will in return enhance the clustering performance. We have conducted extensive experiments on real-world news documents. The results demonstrate that user feedback information coupled with local metric learning can dramatically improve the clustering performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruizhang Huang
    • 1
  • Zhigang Zhang
    • 1
  • Wai Lam
    • 1
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatin, Hong Kong

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