Emerging Pattern Based Classification in Relational Data Mining

  • Michelangelo Ceci
  • Annalisa Appice
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5181)


The usage of descriptive data mining methods for predictive purposes is a recent trend in data mining research. It is well motivated by the understandability of learned models, the limitation of the so-called “horizon effect” and by the fact that it is a multi-task solution. In particular, associative classification, whose main idea is to exploit association rules discovery approaches in classification, gathered a lot of attention in recent years. A similar idea is represented by the use of emerging patterns discovery for classification purposes. Emerging Patterns are classes of regularities whose support significantly changes from one class to another and the main idea is to exploit class characterization provided by discovered emerging patterns for class labeling. In this paper we propose and compare two distinct emerging patterns based classification approaches that work in the relational setting. Experiments empirically prove the effectiveness of both approaches and confirm the advantage with respect to associative classification.


Association Rule Reference Object Association Rule Mining Relational Pattern Incoming Message 
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.


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  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Appice, A., Ceci, M., Malgieri, C., Malerba, D.: Discovering relational emerging patterns. In: Basili, R., Pazienza, M. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 206–217. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Baralis, E., Garza, P.: Majority classification by means of association rules. In: Lavrac, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 35–46. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Ceci, M., Appice, A.: Spatial associative classification: propositional vs structural approach. Journal of Intelligent Information Systems 27(3), 191–213 (2006)CrossRefGoogle Scholar
  5. 5.
    Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under Zeo-Ones loss. Machine Learning 28(2-3), 103–130 (1997)CrossRefGoogle Scholar
  6. 6.
    Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM Press, New York (1999)Google Scholar
  7. 7.
    Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Džeroski, S., Lavrač, N.: Relational Data Mining. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  9. 9.
    Fan, H., Ramamohanarao, K.: An efficient singlescan algorithm for mining essential jumping emerging patterns for classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 456–462 (2002)Google Scholar
  10. 10.
    Fan, H., Ramamohanarao, K.: A bayesian approach to use emerging patterns for classification. In: Australasian Database Conference, vol. 143, pp. 39–48. Australian Computer Society, Inc. (2003)Google Scholar
  11. 11.
    Fan, H., Ramamohanarao, K.: A weighting scheme based on emerging patterns for weighted support vector machines. In: Hu, X., Liu, Q., Skowron, A., Lin, T.Y., Yager, R.R., Zhang, B. (eds.) IEEE International Conference on Granular Computing, pp. 435–440 (2005)Google Scholar
  12. 12.
    Helft, N.: Inductive generalization: a logical framework. In: Progress in Machine Learning, pp. 149–157. Sigma Press (1987)Google Scholar
  13. 13.
    Li, J., Dong, G., Ramamohanarao, K., Wong, L.: DeEPs: A new instance-based lazy discovery and classification system. Machine Learning 54(2), 99–124 (2004)zbMATHCrossRefGoogle Scholar
  14. 14.
    Liu, B., Hsu, W., Ma, Y.: Integrative classification and association rule mining. In: Proceedings of AAAI Conference of Knowledge Discovery in Databases (1998)Google Scholar
  15. 15.
    Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)CrossRefGoogle Scholar
  16. 16.
    Pazzani, M., Mani, S., Shankle, W.: Beyond concise and colorful: learning intelligible rules. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 235–238. AAAI Press, Menlo Park (1997)Google Scholar
  17. 17.
    Plotkin, G.D.: A note on inductive generalization.  5, 153–163 (1970)Google Scholar
  18. 18.
    Robinson, J.A.: A machine oriented logic based on the resolution principle. Journal of the ACM 12, 23–41 (1965)zbMATHCrossRefGoogle Scholar
  19. 19.
    Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: SIAM International Conference on Data Mining (2003)Google Scholar
  20. 20.
    Zhang, X., Dong, G., Ramamohanarao, K.: Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In: Knowledge Discovery and Data Mining, pp. 310–314 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michelangelo Ceci
    • 1
  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

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