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A Constraint Acquisition Method for Data Clustering

  • João M. M. Duarte
  • Ana L. N. Fred
  • Fernando Jorge F. Duarte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

A new constraint acquisition method for parwise-constrained data clustering based on user-feedback is proposed. The method searches for non-redundant intra-cluster and inter-cluster query-candidates, ranks the candidates by decreasing order of interest and, finally, prompts the user the most relevant query-candidates. A comparison between using the original data representation and using a learned representation (obtained from the combination of the pairwise constraints and the original data representation) is also performed. Experimental results shown that the proposed constraint acquisition method and the data representation learning methodology lead to clustering performance improvements.

Keywords

Constraint Acquisition Constrained Data Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • João M. M. Duarte
    • 1
    • 2
  • Ana L. N. Fred
    • 1
  • Fernando Jorge F. Duarte
    • 2
  1. 1.Instituto de Telecomunicações, Instituto Superior TécnicoLisboaPortugal
  2. 2.GECAD - Knowledge Engineering and Decision-Support Research Center, Institute of EngineeringPolytechnic of PortoPortugal

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