Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links

  • M. Eduardo Ares
  • Javier Parapar
  • Álvaro Barreiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5766)


In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effectiveness against previous constrained and non-constrained algorithms for the given task.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. Eduardo Ares
    • 1
  • Javier Parapar
    • 1
  • Álvaro Barreiro
    • 1
  1. 1.IRLab, Department of Computer ScienceUniversity of A CoruñaSpain

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