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Semi-supervised Clustering by Selecting Informative Constraints

  • Vidyadhar Rao
  • C. V. Jawahar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

Traditional clustering algorithms use a predefined metric and no supervision in identifying the partition. Existing semi-supervised clustering approaches either learn a metric from randomly chosen constraints or actively select informative constraints using a generic distance measure like Euclidean norm. We tackle the problem of identifying constraints that are informative to learn appropriate metric for semi-supervised clustering. We propose an approach to simultaneously find out appropriate constraints and learn a metric to boost the clustering performance. We evaluate clustering quality of our approach using the learned metric on the MNIST handwritten digits, Caltech-256 and MSRC2 object image datasets. Our results on these datasets have significant improvements over the baseline methods like MPCK-MEANS.

Keywords

Semi-supervised Clustering Constraint Selection Metric Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vidyadhar Rao
    • 1
  • C. V. Jawahar
    • 1
  1. 1.IIIT-HyderabadIndia

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