Context-Based Distance Learning for Categorical Data Clustering

  • Dino Ienco
  • Ruggero G. Pensa
  • Rosa Meo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5772)


Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of the same categorical attribute, since they are not ordered. In this paper, we propose a method to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute A i can be determined by the way in which the values of the other attributes A j are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of A i a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes A j . We validate our approach on various real world and synthetic datasets, by embedding our distance learning method in both a partitional and a hierarchical clustering algorithm. Experimental results show that our method is competitive w.r.t. categorical data clustering approaches in the state of the art.


Synthetic Dataset Categorical Attribute Normalize Mutual Information Subspace Cluster Categorical Cluster 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2000)zbMATHGoogle Scholar
  2. 2.
    Kasif, S., Salzberg, S., Waltz, D., Rachlin, J., Aha, D.: Towards a framework for memory-based reasoning (manuscript, 1995) (in review)Google Scholar
  3. 3.
    Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283–304 (1998)CrossRefGoogle Scholar
  4. 4.
    Guha, S., Rastogi, R., Shim, K.: Rock: A robust clustering algorithm for categorical attributes. In: Proc. of IEEE ICDE 1999 (1999)Google Scholar
  5. 5.
    Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C.: Scalable clustering of categorical data. In: Proc. of EDBT 2004, pp. 123–146 (2004)Google Scholar
  6. 6.
    Zaki, M.J., Peters, M.: Clicks: Mining subspace clusters in categorical data via k-partite maximal cliques. In: Proc. of IEEE ICDE 2005, pp. 355–356 (2005)Google Scholar
  7. 7.
    Ganti, V., Gehrke, J., Ramakrishnan, R.: Cactus-clustering categorical data using summaries. In: Proc. of ACM SIGKDD 1999, pp. 73–83 (1999)Google Scholar
  8. 8.
    Barbara, D., Couto, J., Li, Y.: Coolcat: an entropy-based algorithm for categorical clustering. In: Proc. of CIKM 2002, pp. 582–589. ACM Press, New York (2002)Google Scholar
  9. 9.
    Li, T., Ma, S., Ogihara, M.: Entropy-based criterion in categorical clustering. In: Proc. of ICML 2004, pp. 536–543 (2004)Google Scholar
  10. 10.
    Ahmad, A., Dey, L.: A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recogn. Lett. 28(1), 110–118 (2007)CrossRefGoogle Scholar
  11. 11.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  12. 12.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proc. of ICML 2003, Washington, DC (2003)Google Scholar
  13. 13.
    Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  14. 14.
    Strehl, A., Ghosh, J., Cardie, C.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583–617 (2002)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998),
  16. 16.
    Melli, G.: Dataset generator, perfect data for an imperfect world (2008),
  17. 17.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Data Management Systems. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dino Ienco
    • 1
  • Ruggero G. Pensa
    • 1
  • Rosa Meo
    • 1
  1. 1.Dept. of Computer ScienceUniversity of TorinoItaly

Personalised recommendations