li-clusteringwith-2010.pdf (433.17 kB)
Clustering with instance and attribute level side information
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 systemsVolume
3Issue
6Pagination
770 - 785Publisher
Atlantis PressLocation
Paris, FranceISSN
1875-6891eISSN
1875-6883Language
engNotes
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).Publication classification
C1 Refereed article in a scholarly journalCopyright notice
2010, The AuthorsUsage metrics
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