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Published Articles >> Table of Contents >> Abstract
Fifth IEEE International Conference on Data Mining (ICDM'05)
pp. 693-696
Semi-Supervised Clustering with Metric Learning Using Relative Comparisons
Nimit Kumar, IBM India Research Lab
Krishna Kummamuru, IBM India Research Lab
Deepa Paranjpe, IBM India Research Lab
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.128
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| Abstract |
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Semi-supervised clustering algorithms partition a given
data set using limited supervision from the user. In this paper,
we propose a clustering algorithmthat uses supervision
in terms of relative comparisons, viz., is closer to than
to . 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 pairwise
constraints for supervision.
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 Additional Information
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Citation:
Nimit Kumar, Krishna Kummamuru, Deepa Paranjpe,
"Semi-Supervised Clustering with Metric Learning Using Relative Comparisons,"
icdm,
pp. 693-696,
Fifth IEEE International Conference on Data Mining (ICDM'05),
2005
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