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2008

Semisupervised Clustering with Metric Learning using Relative Comparisons

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Semisupervised Clustering with Metric Learning using Relative Comparisons
Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also on the kind of dissimilarity measure used while creating partitions of the space. This paper proposes a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The proposed clustering algorithm simultaneously learns the underlying dissimilarity measure while finding compact clusters in the given data set using relative comparisons. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy and is more robust than similar algorithms using pairwise constraints for supervision.
Nimit Kumar, Krishna Kummamuru
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TKDE
Authors Nimit Kumar, Krishna Kummamuru
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