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An effective semi-supervised clustering framework integrating pairwise constraints and attribute preferences
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.
History
Journal
Computing and informaticsVolume
31Issue
3Pagination
597 - 612Publisher
Slovak Academic PressLocation
[Slovakia]ISSN
1335-9150Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2012, Slovak Academic PressUsage metrics
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