Advertisement

Clustering Relational Data Based on Randomized Propositionalization

  • Grant Anderson
  • Bernhard Pfahringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4894)

Abstract

Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into boolean-valued features. Clustering generally is not straightforward to evaluate, but preliminary experimental results on a number of standard ILP datasets show promising results. Clusters generated without class information usually agree well with the true class labels of cluster members, i.e. class distributions inside clusters generally differ significantly from the global class distributions. The two-tiered algorithm described shows good scalability due to the randomized nature of the first step and the availability of efficient propositional clustering algorithms for the second step.

Keywords

clustering propositionalization randomization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55–63 (1998)Google Scholar
  2. 2.
    Camastra, F., Verri, A.: A Novel Kernel Method for Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 801–804 (2005)CrossRefGoogle Scholar
  3. 3.
    Emde, W., Wettschereck, D.: Relational instance-based learning. In: Proceedings of the 13th International Conference on Machine Learning, pp. 122–130 (1996)Google Scholar
  4. 4.
    Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and Distances for Structured Data. Machine Learning 57 (2004)Google Scholar
  5. 5.
    Horváth, T., Wrobel, S., Bohnebeck, U.: Relational Instance-Based Learning with Lists and Terms. Machine Learning 43, 53–80 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Hutchinson, A.: Metrics on terms and clauses. In: Proceedings of the 9th European Conference on Machine Learning, pp. 138–145 (1997)Google Scholar
  7. 7.
    King, R.D., Srinivasan, A., Warmr, L.D.: A Data Mining Tool for Chemical Data Journal of Computer Aided Molecular Design.  15, 173–181 (2001)Google Scholar
  8. 8.
    Kirsten, M., Wrobel, S.: Relational Distance-Based Clustering. In: Proceedings of the 8th International Workshop on Inductive Logic Programming, pp. 261–270 (1998)Google Scholar
  9. 9.
    Kramer, S., Lavrac, N., Flach, P.: Propositionalization Approaches to Relational Data Mining. Relational Data Mining. Springer, Heidelberg (2001)Google Scholar
  10. 10.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  11. 11.
    Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)Google Scholar
  12. 12.
    Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)Google Scholar
  13. 13.
    Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65 (1987)Google Scholar
  14. 14.
    Woźnica, A., Kalousis, A., Hilario, M.: Kernels over Relational Algebra Structures. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 588–598. Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Woźnica, A., Kalousis, A., Hilario, M.: Distance and (Indefinite) Kernels for Sets of Objects. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Zelezny, F., Lavrac, N.: Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62(1-2), 33–63 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Grant Anderson
    • 1
  • Bernhard Pfahringer
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations