Skip to main content
SpringerLink
Log in

Generalization of association rules through disjunction

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

Several efforts were devoted to mining association rules having conjunction of items in premise and conclusion parts. Such rules convey information about the co-occurrence relations between items. However, other links amongst items—like complementary occurrence of items, absence of items, etc.—may occur and offer interesting knowledge to end-users. In this respect, looking for such relationship is a real challenge since not based on the conjunctive patterns. Indeed, catching such links requires obtaining semantically richer association rules, the generalized ones. These latter rules generalize classic ones to also offer disjunction and negation connectors between items, in addition to the conjunctive one. For this purpose, we propose in this paper a complete process for mining generalized association rules starting from an extraction context. Our experimental study stressing on the mining performances as well as the quantitative aspect proves the soundness of our proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Steinbach, M., Kumar, V.: Generalizing the notion of confidence. Knowl. Inf. Syst. 12(3), 279–299 (2007)

    Article  Google Scholar 

  2. Tzanis, G., Berberidis, C.: Mining for mutually exclusive items in transaction databases. Int. J. Data Warehousing Mining 3(3), 45–59 (2007)

    Google Scholar 

  3. Hamrouni, T., Ben Yahia, S., Mephu Nguifo, E.: Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets. Data Knowl. Eng. 68(10), 1091–1111 (2009)

    Article  Google Scholar 

  4. Hamrouni, T.: Mining concise representations of frequent patterns through conjunctive and disjunctive search spaces. Ph.D. thesis, University of Tunis El Manar (Tunisia) and University of Artois (France). Available at: http://tel.archives-ouvertes.fr/tel-00465733 (2009)

  5. Nanavati, A.A., Chitrapura, K.P., Joshi, S., Krishnapuram, R.: Mining generalised disjunctive association rules. In: Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM 2001), pp. 482–489. Atlanta, Georgia, USA (2001)

  6. Hattori, L., dos Santos, G., Cardoso, F., Sampaio, M.: Mining software repositories for software change impact analysis: a case study. In: Proceedings of the 23rd Brasilian Symposium on Database (SBBD 2008). Campinas, Brazil (2008)

  7. She, S.: Feature model mining. Master thesis, University of Waterloo, Waterloo, Ontario, Canada (2008)

  8. Ralbovský, M., Kuchar, T.: Using disjunctions in association mining. In: Proceedings of the 7th Industrial Conference on Data Mining (ICDM 2007). LNCS, vol. 4597, pp. 339–351. Springer, Leipzig, Germany (2007)

    Google Scholar 

  9. Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proceedings of the 21th International Conference on Very Large Data Bases (VLDB 1995), pp. 407–419. Zurich, Switzerland (1995)

    Google Scholar 

  10. Hájek, P., Havel, I., Chytil, M.: The GUHA method of automatic hypotheses determination. Computing 1, 293–308 (1966)

    Article  MATH  Google Scholar 

  11. Hájek, P., Havránek, T.: Mechanizing Hypothesis Formation: Mathematical Foundations for a General Theory. Springer, New York (1978)

    MATH  Google Scholar 

  12. Toivonen, H.: Discovering of frequent patterns in large data collections. Ph.D. thesis, University of Helsinki, Helsinki, Finland (1996)

  13. Weiss, G.M.: Mining with rarity: a unifying framework. ACM-SIGKDD Explor. 6(1), 7–19 (2004)

    Article  Google Scholar 

  14. Hébert, C., Crémilleux, B.: A unified view of objective interestingness measures. In: Proceedings of the 5th International Conference Machine Learning and Data Mining in Pattern Recognition (MLDM 2007). LNCS, vol. 4571, pp. 533–547. Springer, Leipzig, Germany (2007)

    Chapter  Google Scholar 

  15. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–31 (2006)

    Article  Google Scholar 

  16. Casali, A., Cicchetti, R., Lakhal, L.: Essential patterns: a perfect cover of frequent patterns. In: Proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2005). LNCS, vol. 3589, pp. 428–437. Springer, Copenhagen, Denmark (2005)

    Google Scholar 

  17. Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Sys. 22(3), 381–405 (2004)

    Article  Google Scholar 

  18. Ceglar, A., Roddick, J.F.: Association mining. ACM Comput. Surv. 38(2), 1–42 (2006)

    Article  Google Scholar 

  19. Kryszkiewicz, M.: Concise representations of frequent patterns and association rules. Habilitation dissertation, Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland (2002)

  20. Ganter, B., Wille, R.: Formal Concept Analysis. Springer, New York (1999)

    MATH  Google Scholar 

  21. Galambos, J., Simonelli, I.: Bonferroni-type Inequalities with Applications. Springer, New York (2000)

    Google Scholar 

  22. Kryszkiewicz, M.: Closures of downward closed representations of frequent patterns. In: Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009). LNCS, vol. 5572, pp. 104–112. Springer, Salamanca, Spain (2009)

    Google Scholar 

  23. Rauch, J.: Logic of association rules. Appl. Intell. 22(1), 9–28 (2005)

    Article  MATH  Google Scholar 

  24. Hamrouni, T., Ben Yahia, S., Mephu Nguifo, E.: Optimized mining of a concise representation for frequent patterns based on disjunctions rather than conjunctions. In: Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010), pp. 422–427. AAAI, Daytona Beach, Florida, USA (2010)

    Google Scholar 

  25. Baixeries, J., Szathmary, L., Valtchev, P., Godin, R.: Yet a faster algorithm for building the Hasse Diagram of a concept lattice. In: Proceedings of the 7th International Conference on Formal Concept Analysis (ICFCA 2009). LNCS, vol. 5548, pp. 162–177. Springer, Darmstadt, Germany (2009)

    Google Scholar 

  26. Valtchev, P., Missaoui, R., Lebrun, P.: A fast algorithm for building the Hasse Diagram of a Galois Lattice. In: Proceedings of the Conference on Combinatorics, Computer Science and Applications (LaCIM 2000), pp. 293–306. Montréal, Canada (2000)

    Google Scholar 

  27. Cheng, J., Ke, Y., Ng, W.: Effective elimination of redundant association rules. Data Min. Knowl. Discov. 16(2), 221–249 (2008)

    Article  MathSciNet  Google Scholar 

  28. Savasere, A., Omiecinski, E., Navathe, S.: Mining for strong negative associations in a large database of customer transactions. In: Proceedings of the 14th International Conference on Data Engineering (ICDE 1998), pp. 494–502. IEEE Computer Society, Orlando, Florida, USA (1998)

    Chapter  Google Scholar 

  29. Antonie, M., Zaïane, O.R.: Mining positive and negative association rules: an approach for confined rules. In: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2004). LNCS, vol. 3202, pp. 27–38. Springer, Pisa, Italy (2004)

    Google Scholar 

  30. Morzy, M.: Efficient mining of dissociation rules. In: Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006). LNCS, vol. 4081, pp. 228–237. Springer, Krakow, Poland (2006)

    Google Scholar 

  31. Hájek P. (guest ed.): Int. J. Man-Mach. Stud. 10 (1978, special issue on GUHA)

  32. Hájek P. (guest ed.): Int. J. Man-Mach. Stud. 15 (1981, second special issue on GUHA)

  33. Hájek, P., Holena, M., Rauch, J.: The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76(1), 34–48 (2010)

    Article  MATH  Google Scholar 

  34. Rauch, J., Simunek, M.: Dealing with background knowledge in the SEWEBAR project. In: Berendt, B. et al. (eds.) Knowledge Discovery Enhanced with Semantic and Social Information, SCI 220, pp 89–106. Springer, Berlin (2009)

    Chapter  Google Scholar 

  35. Rauch, J.: Classes of association rules: an overview. In: Lin, T., et al. (eds.) Data Mining: Foundations and Practice, SCI 118, pp. 315–337. Springer, Berlin (2008)

    Chapter  Google Scholar 

  36. Kim, H.D.: Complementary occurrence and disjunctive rules for market basket analysis in data mining. In: Proceedings of the 2nd IASTED International Conference Information and Knowledge Sharing (IKS 2003), pp. 155–157. Scottsdale, AZ, USA (2003)

  37. Grün, G.A.: New forms of association rules. Technical Report TR 1998-15, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada (1998)

  38. Shima, Y., Hirata, K., Harao, M., Yokoyama, S., Matsuoka, K., Izumi, T.: Extracting disjunctive closed rules from MRSA data. In: Proceedings of the 1st International Conference on Complex Medical Engineering (CME 2005), pp. 321–325. Takamatsu, Japan (2005)

  39. Shima, Y., Mitsuishi, S., Hirata, K., Harao, M.: Extracting minimal and closed monotone DNF formulas. In: Proceedings of the 7th International Conference Discovery Science (DS 2004). LNCS, vol. 3245, pp. 298–305. Springer, Padova, Italy (2004)

    Google Scholar 

  40. Elble, J., Heeren, C., Pitt, L.: Optimized disjunctive association rules via sampling. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), pp. 43–50. Melbourne, Florida, USA (2003)

  41. Li, J., Jones, J.: Using multiple and negative target rules to make classifiers more understandable. Knowl.-Based Syst. 19(6), 438–444 (2006)

    Article  Google Scholar 

  42. Bykowski, A., Rigotti, C.: DBC: a condensed representation of frequent patterns for efficient mining. Inf. Syst. 28(8), 949–977 (2003)

    Article  Google Scholar 

  43. Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. 15(1), 57–69 (2003)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences of Tunis, University Campus, 1060, Tunis, Tunisia

    Tarek Hamrouni & Sadok Ben Yahia

  2. CRIL-CNRS, Lille Nord University, Artois, France

    Tarek Hamrouni

  3. LIMOS-CNRS, Blaise Pascal University, Campus des cézeaux, 63173, Aubière cedex, France

    Engelbert Mephu Nguifo

Authors
  1. Tarek Hamrouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadok Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Engelbert Mephu Nguifo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tarek Hamrouni.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hamrouni, T., Ben Yahia, S. & Mephu Nguifo, E. Generalization of association rules through disjunction. Ann Math Artif Intell 59, 201–222 (2010). https://doi.org/10.1007/s10472-010-9192-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10472-010-9192-z

Keywords

Mathematics Subject Classifications (2010)

Search

Navigation