Greedy Algorithm for Optimization of Association Rules Relative to Length

  • Beata ZieloskoEmail author
  • Marek Robaszkiewicz
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


In the paper, an optimization of \(\alpha \)-Association rules constructed by greedy algorithm is proposed. It allows us to decrease the number of rules and obtain short rules, what is important from the point of view of knowledge representation. Experimental results for data sets from UCI Machine Learning Respository are presented.


Rough sets Length Greedy algorithm Association rules 


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB, pp. 487–499. Morgan Kaufmann (1994)Google Scholar
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD ’93, pp. 207–216. ACM (1993)Google Scholar
  3. 3.
    Borgelt, C.: Simple algorithms for frequent item set mining. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II, Studies in Computational Intelligence, vol. 263, pp. 351–369. Springer, Berlin Heidelberg (2010)Google Scholar
  4. 4.
    Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. 15th Conference on Computational Statistics (Compstat 2002. Berlin, Germany), pp. 395–400. Physica Verlag, Heidelberg (2002)Google Scholar
  5. 5.
    Feige, U.: A threshold of \(\ln n\) for approximating set cover. In: Leighton, F.T. (ed.) Journal of the ACM (JACM), vol. 45, pp. 634–652. ACM New York (1998)Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Herawan, T., Deris, M.M.: A soft set approach for association rules mining. Knowled.-Based Syst. 24(1), 186–195 (2011)CrossRefGoogle Scholar
  8. 8.
    Kozak, J., Boryczka, U.: Multiple boosting in the ant colony decision forest meta-classifier. Knowled.-Based Syst. 75, 141–151 (2015)CrossRefGoogle Scholar
  9. 9.
    Lichman, M.: UCI Machine Learning Repository. Accessed Feb 2016
  10. 10.
    Moshkov, M.J., Skowron, A., Suraj, Z.: On minimal rule sets for almost all binary information systems. Fundam. Inform. 80(1–3), 247–258 (2007)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Moshkov, M.J., Piliszczuk, M., Zielosko, B.: On construction of partial association rules. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT, LNCS, vol. 5589, pp. 176–183. Springer (2009)Google Scholar
  12. 12.
    Moshkov, M.J., Piliszczuk, M., Zielosko, B.: Greedy algorithm for construction of partial association rules. Fundam. Inform. 92(3), 259–277 (2009)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Nguyen, H.S., Ślȩzak, D.: Approximate reducts and association rules - correspondence and complexity results. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC, LNCS, vol. 1711, pp. 137–145. Springer (1999)Google Scholar
  14. 14.
    Park, J.S., Chen, M.S., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Carey, M.J., Schneider, D.A. (eds.) SIGMOD Conference, pp. 175–186. ACM Press (1995)Google Scholar
  15. 15.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)CrossRefzbMATHGoogle Scholar
  17. 17.
    Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) VLDB, pp. 432–444. Morgan Kaufmann (1995)Google Scholar
  18. 18.
    Skowron, A.: Rough sets in KDD - plenary talk. In: Shi, Z., Faltings, B., Musen, M. (eds.) Proceedings of the 16th IFIP, pp. 1–14. World Computer Congress, Publishing House of Electronic Industry (2000)Google Scholar
  19. 19.
    Slavík, P.: A tight analysis of the greedy algorithm for set cover. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing. pp. 435–441. ACM New York (1996)Google Scholar
  20. 20.
    Stańczyk, U.: Selection of decision rules based on attribute ranking. J. Intell. Fuzzy Syst. 29(2), 899–915 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. Int. J. Intell. Syst. 16(1), 13–27 (2001)CrossRefzbMATHGoogle Scholar
  22. 22.
    Tkacz, M.A.: Artificial neural networks in incomplete data sets processing. In: Kopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) IIS: IIPWM’05. pp. 577–584. Advances in Soft Computing, Springer (2005)Google Scholar
  23. 23.
    Wieczorek, A., Słowiński, R.: Generating a set of association and decision rules with statistically representative support and anti-support. Inf. Sci. 277, 56–70 (2014)CrossRefGoogle Scholar
  24. 24.
    Zielosko, B.: Greedy algorithm for construction of partial association rules. Studia Inform. 31(2A), 225–236 (2010) (in Polish)Google Scholar
  25. 25.
    Zielosko, B.: Global optimization of exact association rules relative to coverage. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybiński, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 428–437. Springer (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer Science, University of SilesiaSosnowiecPoland
  2. 2.EL-PLUSChorzówPoland

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