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Mining Association Rules for Label Ranking

  • Cláudio Rebelo de Sá
  • Carlos Soares
  • Alípio Mário Jorge
  • Paulo Azevedo
  • Joaquim Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)

Abstract

Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Keywords

Association Rule Discretization Method Mining Association Rule Minimum Entropy Support Count 
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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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Aiguzhinov, A., Soares, C., Serra, A.P.: A similarity-based adaptation of naive bayes for label ranking: Application to the metalearning problem of algorithm recommendation. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 16–26. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Azevedo, P.J., Jorge, A.M.: Ensembles of jittered association rule classifiers. Data Min. Knowl. Discov. 21(1), 91–129 (2010)CrossRefGoogle Scholar
  4. 4.
    Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery 4(2), 217–240 (2000)CrossRefGoogle Scholar
  5. 5.
    Brazdil, P., Soares, C., Costa, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 50(3), 251–277 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD 1997, pp. 255–264 (1997)Google Scholar
  7. 7.
    Cheng, W., Hühn, J., Hüllermeier, E.: Decision tree and instance-based learning for label ranking. In: ICML 2009: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 161–168. ACM, New York (2009)Google Scholar
  8. 8.
    Pinto da Costa, J., Soares, C.: A weighted rank measure of correlation. Australian & New Zealand Journal of Statistics 47(4), 515–529 (2005)CrossRefzbMATHGoogle Scholar
  9. 9.
    Dekel, O., Manning, C.D., Singer, Y.: Log-linear models for label ranking. Advances in Neural Information Processing Systems (2003)Google Scholar
  10. 10.
    Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Machine Learning - International Workshop Then Conference, pp. 194–202 (1995)Google Scholar
  11. 11.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)Google Scholar
  12. 12.
    Fürnkranz, J., Hüllermeier, E.: Preference learning. KI 19(1), 60 (2005)zbMATHGoogle Scholar
  13. 13.
    Har-Peled, S., Roth, D., Zimak, D.: Constraint classification: A new approach to multiclass classification. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds.) ALT 2002. LNCS (LNAI), vol. 2533, pp. 365–379. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17), 1897–1916 (2008)CrossRefzbMATHGoogle Scholar
  15. 15.
    Kemeny, J., Snell, J.: Mathematical Models in the Social Sciences. MIT Press, Cambridge (1972)zbMATHGoogle Scholar
  16. 16.
    Kendall, M., Gibbons, J.: Rank correlation methods. Griffin, London (1970)zbMATHGoogle Scholar
  17. 17.
    Lebanon, G., Lafferty, J.D.: Conditional Models on the Ranking Poset. In: NIPS, pp. 415–422 (2002)Google Scholar
  18. 18.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining, pp. 80–86 (1998)Google Scholar
  19. 19.
    Park, J.S., Chen, M.S., Yu, P.S.: An effective hash-based algorithm for mining association rules. ACM SIGMOD Record 24(2), 175–186 (1995)CrossRefGoogle Scholar
  20. 20.
    Park, J.S., Chen, M.S., Yu, P.S.: Efficient parallel and data mining for association rules. In: CIKM, pp. 31–36 (1995)Google Scholar
  21. 21.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010), http://www.R-project.org ISBN 3-900051-07-0
  22. 22.
    Spearman, C.: The proof and measurement of association between two things. American Journal of Psychology 15, 72–101 (1904)CrossRefGoogle Scholar
  23. 23.
    Thomas, S., Sarawagi, S.: Mining generalized association rules and sequential patterns using sql queries. In: KDD, pp. 344–348 (1998)Google Scholar
  24. 24.
    Todorovski, L., Blockeel, H., Džeroski, S.: Ranking with Predictive Clustering Trees. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 444–455. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  25. 25.
    Vembu, S., Gärtner, T.: Label Ranking Algorithms: A Survey. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cláudio Rebelo de Sá
    • 1
  • Carlos Soares
    • 1
    • 2
  • Alípio Mário Jorge
    • 1
    • 3
  • Paulo Azevedo
    • 5
  • Joaquim Costa
    • 4
  1. 1.LIAAD-INESC Porto L.A.PortoPortugal
  2. 2.Faculdade de EconomiaUniversidade do PortoPortugal
  3. 3.DCC - Faculdade de CienciasUniversidade do PortoPortugal
  4. 4.DM - Faculdade de CienciasUniversidade do PortoPortugal
  5. 5.CCTC, Departamento de InformáticaUniversidade do MinhoPortugal

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