CICE-BCubed: A New Evaluation Measure for Overlapping Clustering Algorithms

  • Henry Rosales-Méndez
  • Yunior Ramírez-Cruz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

The evaluation of clustering algorithms is a field of Pattern Recognition still open to extensive debate. Most quality measures found in the literature have been conceived to evaluate non-overlapping clusterings, even when most real-life problems are better modeled using overlapping clustering algorithms. A number of desirable conditions to be satisfied by quality measures used to evaluate clustering algorithms have been proposed, but measures fulfilling all conditions still fail to adequately handle several phenomena arising in overlapping clustering. In this paper, we focus on a particular case of such desirable conditions, which existing measures that fulfill previously enunciated conditions fail to satisfy. We propose a new evaluation measure that correctly handles the studied phenomenon for the case of overlapping clusterings, while still satisfying the previously existing conditions.

Keywords

Cluster Algorithm Evaluation Measure Computational Linguistics Object Pair Desirable Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval 12(4), 461–486 (2009)CrossRefGoogle Scholar
  2. 2.
    Bagga, A., Baldwin, B.: Entity-based cross-document coreferencing using the vector space model. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, pp. 79–85 (1998)Google Scholar
  3. 3.
    Meilă, M.: Comparing Clusterings by the Variation of Information. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 173–187. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Dom, B.: An information-theoretic external cluster-validity measure. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 137–145 (2002)Google Scholar
  5. 5.
    Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 410–420 (2007)Google Scholar
  6. 6.
    van Rijsbergen, C.: Foundation of evaluation. Journal of Documentation 30(4), 365–373 (1974)CrossRefGoogle Scholar
  7. 7.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17(2-3), 107–145 (2001)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Henry Rosales-Méndez
    • 1
  • Yunior Ramírez-Cruz
    • 2
  1. 1.Computer Science DepartmentUniversidad de OrienteSantiago de CubaCuba
  2. 2.Center for Pattern Recognition and Data MiningUniversidad de OrienteSantiago de CubaCuba

Personalised recommendations