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Clustering with instance and attribute level side information

Wang, Jinlong, Wu, Shunyao and Li, Gang 2010, Clustering with instance and attribute level side information, International journal of computational intelligence systems, vol. 3, no. 6, pp. 770-785.

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Title Clustering with instance and attribute level side information
Author(s) Wang, Jinlong
Wu, Shunyao
Li, Gang
Journal name International journal of computational intelligence systems
Volume number 3
Issue number 6
Start page 770
End page 785
Total pages 16
Publisher Atlantis Press
Place of publication Paris, France
Publication date 2010-12
ISSN 1875-6891
1875-6883
Keyword(s) data mining
clustering
semi-supervised learning
constraints
Summary 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.
Notes Atlantis Press adheres to the principles of Creative Commons, meaning that we do not claim copyright of the work we publish. We only ask people using one of our publications to respect the integrity of the work and to refer to the original location, title and author(s).
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032946

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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