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Clustering with instance and attribute level side information

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journal contribution
posted on 2010-12-01, 00:00 authored by J Wang, S Wu, Gang LiGang Li
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

History

Journal

International journal of computational intelligence systems

Volume

3

Pagination

770 - 785

Location

Paris, France

Open access

  • Yes

ISSN

1875-6891

eISSN

1875-6883

Language

eng

Notes

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Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2010, The Authors

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