Abstract
Association rule mining often results in an overwhelming number of rules. In practice, it is difficult for the final user to select the most relevant rules. In order to tackle this problem, various interestingness measures were proposed. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we give a unified view of objective interestingness measures. We define a new framework embedding a large set of measures called SBMs and we prove that the SBMs have a similar behavior. Furthermore, we identify the whole collection of the rules simultaneously optimizing all the SBMs. We provide an algorithm to efficiently mine a reduced set of rules among the rules optimizing all the SBMs. Experiments on real datasets highlight the characteristics of such rules.
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Keywords
- Association Rule
- Rule Mining
- Mining Association Rule
- Inductive Logic Programming
- Interestingness Measure
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
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) SIGMOD 1993 Conference, pp. 207–216. ACM Press, New York (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB 1994. Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 12-15, 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Bayardo, J.R.J., Agrawal, R.: Mining the most interesting rules. In: KDD 1999, pp. 145–154 (1999)
Blanchard, J., Guillet, F., Briand, H.: A user-driven and quality-oriented visualization for mining association rules. In: ICDM 2003. The 3rd IEEE International Conference on Data Mining, pp. 493–496. IEEE Computer Society Press, Los Alamitos (2003)
Boulicaut, J.-F., Bykowski, A., Rigotti, C.: Approximation of frequency queries by means of free-sets. In: Zighed, D.A., Komorowski, H.J., Zytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 75–85. Springer, Heidelberg (2000)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. In: Peckham, J. (ed.) SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, USA, May 13-15, 1997, pp. 265–276. ACM Press, New York (1997)
Francisci, D., Collard, M.: Multi-criteria evaluation of interesting dependencies according to a data mining approach. In: Congress on Evolutionary Computation, Canberra, Australia, 12, pp. 1568–1574. IEEE Computer Society Press, Los Alamitos (2003)
Fürnkranz, J., Flach, P.A.: Roc ’n’ rule learning-towards a better understanding of covering algorithms. Machine Learning 58(1), 39–77 (2005)
Gasmi, G., Yahia, S.B., Nguifo, E.M., Slimani, Y.: Igb: A new informative generic base of association rules. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 81–90. Springer, Heidelberg (2005)
Hébert, C., Crémilleux, B.: Optimized rule mining through a unified framework for interestingness measures. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 238–247. Springer, Heidelberg (2006)
Hilderman, R.J., Hamilton, H.J.: Measuring the interestingness of discovered knowledge: A principled approach. Intell. Data Anal. 7(4), 347–382 (2003)
Hirano, S., Tsumoto, S.: Guide to the hepatitis data. In: PKDD 2005. ECML/PKDD’05 Discovery Challenge on hepatitis data co-located with the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 2005, pp. 120–124 (2005)
Kryszkiewicz, M.: Concise representations of association rules. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 92–109. Springer, Heidelberg (2002)
Lavrac, N., Flach, P., Zupan, B.: Rule evaluation measures: a unifying view. In: Džeroski, S., Flach, P.A. (eds.) Inductive Logic Programming. LNCS (LNAI), vol. 1634, pp. 174–185. Springer, Heidelberg (1999)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 299–312. Springer, Heidelberg (1998)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press, Cambridge (1991)
Plasse, M., Niang, N., Saporta, G., Leblond, L.: Une comparaison de certains indices de pertinence des règles d’association. In: Ritschard, G., Djeraba, C. (eds.) EGC. Revue des Nouvelles Technologies de l’Information, vol. RNTI-E-6, pp. 561–568. Cépaduès-Éditions (2006)
Sebag, M., Schoenauer, M.: Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In: Boose, M.L.J., Gaines, B. (eds.) EKAW 1988. European Knowledge Acquisistion Workshop, pages 28–1–28–20 (1988)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: KDD, pp. 32–41. ACM, New York (2002)
Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: The 7th International Conference on Discovery Science, 10, 2004, pp. 290–297 (2004)
Wille, R.: chapter Restructuring lattice theory: an approach based on hierachies of concepts. In: Ordered sets, pp. 445–470. Reidel, Dordrecht (1982)
Zaki, M.J.: Generating non-redundant association rules. In: KDD 2000, pp. 34–43 (2000)
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Hébert, C., Crémilleux, B. (2007). A Unified View of Objective Interestingness Measures. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_40
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DOI: https://doi.org/10.1007/978-3-540-73499-4_40
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