Advertisement

Distribution Rules with Numeric Attributes of Interest

  • Alípio M. Jorge
  • Paulo J. Azevedo
  • Fernando Pereira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

In this paper we introduce distribution rules, a kind of association rules with a distribution on the consequent. Distribution rules are related to quantitative association rules but can be seen as a more fundamental concept, useful for learning distributions. We formalize the main concepts and indicate applications to tasks such as frequent pattern discovery, sub group discovery and forecasting. An efficient algorithm for the generation of distribution rules is described. We also provide interest measures, visualization techniques and evaluation.

Keywords

Association Rule Minimal Support Frequent Itemset Distribution Rule Subgroup Discovery 
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.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
  2. 2.
    Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. Journal of Intelligent Information Systems (2003)Google Scholar
  3. 3.
    Azevedo, P.J., Jorge, A.M.: The class project (2006), http://www.niaad.liacc.up.pt/~amjorge/Projectos/Class/
  4. 4.
    Bayardo, R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. In: ICDE, pp. 188–197. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  5. 5.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Peckham, J. (ed.) Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, June 13–15, pp. 255–264 (1997)Google Scholar
  6. 6.
    Carney, M., Cunningham, P., Dowling, J., Lee, C.: Predicting probability distributions for surf height using an ensemble of mixture density networks. In: Proceedings of the 22 International Conference on Machine Learning, ICML 2005, Bonn, Germany (2005)Google Scholar
  7. 7.
    Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. John Wiley & Sons, New York (1999)Google Scholar
  8. 8.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)Google Scholar
  9. 9.
    Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization. In: SIGMOD 1996: Proceedings of the 1996 ACM SIGMOD international conference on Management of data, pp. 13–23. ACM Press, New York (1996)CrossRefGoogle Scholar
  10. 10.
    Han, E.-H., Karypis, G., Kumar, V., Mobasher, B.: Clustering based on association rule hypergraphs. In: Research Issues on Data Mining and Knowledge Discovery (1997)Google Scholar
  11. 11.
    Kavsek, B., Lavrac, N., Jovanoski, V.: Apriori-sd: Adapting association rule learning to subgroup discovery. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 230–241. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Schapire, R.E., Sellie, L.: On the learnability of discrete distributions. In: STOC 1994: Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, pp. 273–282. ACM Press, New York (1994)CrossRefGoogle Scholar
  13. 13.
    Klősgen, W.: Explora: A multipattern and multistrategy discovery assistant. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)Google Scholar
  14. 14.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998: Proceedings of the fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. ACM Press, New York (1998)Google Scholar
  15. 15.
    Liu, B., Hsu, W., Ma, Y.: Pruning and summarizing the discovered associations. In: KDD 1999: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 125–134. ACM Press, New York (1999)CrossRefGoogle Scholar
  16. 16.
    Merz, C.J., Murphy, P.: Uci repository of machine learning database (1996), http://www.cs.uci.edu/~mlearn
  17. 17.
    Ozgur, A., Tan, P.-N., Kumar, V.: Rba: An integrated framework for regression based on association rules. In: Berry, M.W., Dayal, U., Kamath, C., Skillicorn, D.B. (eds.) SDM 2004: Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA (2004)Google Scholar
  18. 18.
    Silberschatz, A., Tuzhilin, A.: On subjective measure of interestingness in knowledge discovery. In: KDD 1995: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 275–281. AAAi Press, Menlo Park (1995)Google Scholar
  19. 19.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: SIGMOD 1996: Proceedings of the 1996 ACM SIGMOD international conference on Management of data, pp. 1–12. ACM Press, New York (1996)CrossRefGoogle Scholar
  20. 20.
    Webb, G.I.: Discovering associations with numeric variables. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 383–388. ACM Press, New York (2001)CrossRefGoogle Scholar
  21. 21.
    Zhang, H., Padmanabhan, B., Tuzhilin, A.: On the discovery of significant statistical quantitative rules. In: KDD 2004: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 374–383. ACM Press, New York (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alípio M. Jorge
    • 1
  • Paulo J. Azevedo
    • 2
  • Fernando Pereira
    • 1
  1. 1.LIACC, Faculty of EconomicsUniversity of PortoPortugal
  2. 2.Departamento de InformáticaUniversity of MinhoPortugal

Personalised recommendations