Semi-supervised clustering with metric learning using relative comparisons

Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. In this paper, we propose a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The success of a clustering algorithm also depends on t...

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Bibliographic Details
Published inFifth IEEE International Conference on Data Mining (ICDM'05) p. 4 pp.
Main Authors Kumar, N., Kummamuru, K., Paranjpe, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Clustering algorithms
Data mining
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Nearest neighbor searches
Partitioning algorithms
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Summary:Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. In this paper, we propose a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The success of a clustering algorithm also depends on the kind of dissimilarity measure. The proposed clustering algorithm learns the underlying dissimilarity measure while finding compact clusters in the given data set. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy than the algorithms using pair-wise constraints for supervision.
ISBN:9780769522784
0769522785
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2005.128