Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Note that the simplification of the mining process here refers to a reduction of complexity in terms of the number of rules analysed, i.e. search space size. Algorithmic contributions and therefore complexity analyses regarding efficient implementations of the proposed approach are left for future work.
- 2.
Indeed all the measures used in our approach take values in the interval [0, 1], then a measure k to minimize can be changed to a measure to maximize by considering \(1-g_k(r)\) instead of \(g_k(r)\).
- 3.
Evaluating support and confidence of \(\overline{A} \rightarrow B\) and \(\overline{A} \rightarrow \overline{B}\) can lead to undefined values, e.g. evaluating \(\overline{A} \rightarrow B\), we have \(Bel(\overline{A} \times B) = 0\) when \(\overline{A}\) has never been observed, leading to \(Bel(B |\overline{A})\) being undefined. However, pruning using dominance and Electre I requires the same measures to be defined. Undefined values are thus substituted by an arbitrary value that neither favor nor penalize the evaluation of the rule \(A \rightarrow B\). The median of \(Bel(\overline{A} \times B)\) (resp. \(Bel(\overline{A} \times \overline{B})\)) has been chosen. Note that \(A \rightarrow \overline{B}\) is not concerned since evaluating \(A\rightarrow B\) implies evidence on A.
References
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)
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)
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)
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)
Chen, M.C.: Ranking discovered rules from data mining with multiple criteria by data envelopment analysis. Expert Syst. Appl. 33(4), 1110–1116 (2007)
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)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)
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)
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). https://doi.org/10.1007/978-3-642-29461-7_45
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). http://dl.acm.org/citation.cfm?id=647233.760137
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)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9-es (2006)
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)
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)
Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)
Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the subjective interestigness of association rules. IEEE Intell. Syst. 15(5), 47–55 (2000). https://doi.org/10.1109/5254.889106
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). https://doi.org/10.1007/978-3-642-01715-5_4
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)
Samet, A., Lefèvre, E., Yahia, S.B.: Evidential data mining: precise support and confidence. J. Intell. Inf. Syst. 47(1), 135–163 (2016)
Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in wordNet. In: Ecai, vol. 16, p. 1089 (2004)
Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowl. Data Eng. 8(6), 970–974 (1996)
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)
Tobji, M.B., Yaghlane, B.B., Mellouli, K.: A new algorithm for mining frequent itemsets from evidential databases. Proc. IPMU 8, 1535–1542 (2008)
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). https://doi.org/10.1007/978-3-540-87993-0_4
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)
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). https://doi.org/10.1007/978-3-540-30214-8_23
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
L’Héritier, C., Harispe, S., Imoussaten, A., Dusserre, G., Roig, B. (2019). Selecting Relevant Association Rules From Imperfect Data. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-35514-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35513-5
Online ISBN: 978-3-030-35514-2
eBook Packages: Computer ScienceComputer Science (R0)