Selecting Relevant Association Rules From Imperfect Data

  • Cécile L’HéritierEmail author
  • Sébastien Harispe
  • Abdelhak Imoussaten
  • Gilles Dusserre
  • Benoît Roig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)


Association Rule Mining (ARM) in the context of imperfect data (e.g. imprecise data) has received little attention so far despite the prevalence of such data in a wide range of real-world applications. In this work, we present an ARM approach that can be used to handle imprecise data and derive imprecise rules. Based on evidence theory and Multiple Criteria Decision Analysis, the proposed approach relies on a selection procedure for identifying the most relevant rules while considering information characterizing their interestingness. The several measures of interestingness defined for comparing the rules as well as the selection procedure are presented. We also show how a priori knowledge about attribute values defined into domain taxonomies can be used to (i) ease the mining process, and to (ii) help identifying relevant rules for a domain of interest. Our approach is illustrated using a concrete simplified case study related to humanitarian projects analysis.


Association rules Imperfect data Evidence theory Multiple Criteria Decision Analysis (MCDA) 


  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)Google Scholar
  3. 3.
    Ait-Mlouk, A., Gharnati, F., Agouti, T.: Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach. Vietnam J. Comput. Sci. 3(4), 235–245 (2016)CrossRefGoogle Scholar
  4. 4.
    Bouker, S., Saidi, R., Yahia, S.B., Nguifo, E.M.: Ranking and selecting association rules based on dominance relationship. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 1, pp. 658–665. IEEE (2012)Google Scholar
  5. 5.
    Chen, M.C.: Ranking discovered rules from data mining with multiple criteria by data envelopment analysis. Expert Syst. Appl. 33(4), 1110–1116 (2007)CrossRefGoogle Scholar
  6. 6.
    Choi, D.H., Ahn, B.S., Kim, S.H.: Prioritization of association rules in data mining: multiple criteria decision approach. Expert Syst. Appl. 29(4), 867–878 (2005)CrossRefGoogle Scholar
  7. 7.
    Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Djouadi, Y., Redaoui, S., Amroun, K.: Mining association rules under imprecision and vagueness: towards a possibilistic approach. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1–6. IEEE (2007)Google Scholar
  9. 9.
    Dubois, D., Denoeux, T.: Conditioning in dempster-shafer theory: prediction vs. revision. In: Denoeux, T., Masson, M.H. (eds.) Belief Functions: Theory and Applications, pp. 385–392. Springer, Heidelberg (2012). Scholar
  10. 10.
    Fagin, R., Halpern, J.Y.: A new approach to updating beliefs. In: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, UAI 1990, pp. 347–374. Elsevier Science Inc., New York, NY, USA (1991).
  11. 11.
    Figueira, J., Roy, B.: Determining the weights of criteria in the electre type methods with a revised simos’ procedure. Eur. J. Oper. Res. 139(2), 317–326 (2002)CrossRefGoogle Scholar
  12. 12.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9-es (2006)CrossRefGoogle Scholar
  13. 13.
    Hewawasam, K., Premaratne, K., Subasingha, S., Shyu, M.L.: Rule mining and classification in imperfect databases. In: 2005 7th International Conference on Information Fusion, vol. 1, p. 8. IEEE (2005)Google Scholar
  14. 14.
    Hong, T.P., Lin, K.Y., Wang, S.L.: Fuzzy data mining for interesting generalized association rules. Fuzzy Sets Syst. 138(2), 255–269 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)Google Scholar
  16. 16.
    Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the subjective interestigness of association rules. IEEE Intell. Syst. 15(5), 47–55 (2000). Scholar
  17. 17.
    Nguyen Le, T.T., Huynh, H.X., Guillet, F.: Finding the most interesting association rules by aggregating objective interestingness measures. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 40–49. Springer, Heidelberg (2009). Scholar
  18. 18.
    Roy, B.: Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle 2(8), 57–75 (1968)CrossRefGoogle Scholar
  19. 19.
    Samet, A., Lefèvre, E., Yahia, S.B.: Evidential data mining: precise support and confidence. J. Intell. Inf. Syst. 47(1), 135–163 (2016)CrossRefGoogle Scholar
  20. 20.
    Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in wordNet. In: Ecai, vol. 16, p. 1089 (2004)Google Scholar
  21. 21.
    Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  22. 22.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowl. Data Eng. 8(6), 970–974 (1996)CrossRefGoogle Scholar
  23. 23.
    Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 32–41. ACM (2002)Google Scholar
  24. 24.
    Tobji, M.B., Yaghlane, B.B., Mellouli, K.: A new algorithm for mining frequent itemsets from evidential databases. Proc. IPMU 8, 1535–1542 (2008)Google Scholar
  25. 25.
    Bach Tobji, M.A., Ben Yaghlane, B., Mellouli, K.: Frequent itemset mining from databases including one evidential attribute. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 19–32. Springer, Heidelberg (2008). Scholar
  26. 26.
    Toloo, M., Sohrabi, B., Nalchigar, S.: A new method for ranking discovered rules from data mining by dea. Expert Syst. Appl. 36(4), 8503–8508 (2009)CrossRefGoogle Scholar
  27. 27.
    Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 290–297. Springer, Heidelberg (2004). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cécile L’Héritier
    • 1
    • 2
    Email author
  • Sébastien Harispe
    • 1
  • Abdelhak Imoussaten
    • 1
  • Gilles Dusserre
    • 1
  • Benoît Roig
    • 2
  1. 1.LGI2P, IMT Mines Ales, Univ MontpellierAlèsFrance
  2. 2.EA7352 CHROMEUniversité de NîmesNîmesFrance

Personalised recommendations