An effective semi-supervised clustering framework integrating pairwise constraints and attribute preferences

Wang, Jinlong, Wu, Shunyao, Wen, Can and Li, Gang 2012, An effective semi-supervised clustering framework integrating pairwise constraints and attribute preferences, Computing and informatics, vol. 31, no. 3, pp. 597-612.

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Title An effective semi-supervised clustering framework integrating pairwise constraints and attribute preferences
Author(s) Wang, Jinlong
Wu, Shunyao
Wen, Can
Li, Gang
Journal name Computing and informatics
Volume number 31
Issue number 3
Start page 597
End page 612
Total pages 1
Publisher Slovak Academic Press
Place of publication [Slovakia]
Publication date 2012
ISSN 1335-9150
Keyword(s) semi-supervised clustering
pairwise
attribute preference
Summary Both the instance level knowledge and the attribute level knowledge can improve clustering quality, but how to effectively utilize both of them is an essential problem to solve. This paper proposes a wrapper framework for semi-supervised clustering, which aims to gracely integrate both kinds of priori knowledge in the clustering process, the instance level knowledge in the form of pairwise constraints and the attribute level knowledge in the form of attribute order preferences. The wrapped algorithm is then designed as a semi-supervised clustering process which transforms this clustering problem into an optimization problem. The experimental results demonstrate the effectiveness and potential of proposed method.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Slovak Academic Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048125

Document type: Journal Article
Collection: School of Information Technology
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