Abstract
Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand. The algorithms for closed and maximal itemsets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined. In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. Empirical results confirm that with a proper combination of data mining and statistical analysis, a large number of non-significant, redundant and contradictive rules can be eliminated while preserving relatively high precision and recall. More importantly, the results reveal the important characteristics and differences between using frequent, closed and maximal itemsets for the classification task, and the effect of incorporating statistical/heuristic measures for optimizing such rule sets. With closed itemset mining already being a preferred choice for complexity and redundancy reduction during rule generation, this study has further confirmed that overall closed itemset based association rules are also of better quality in terms of classification precision and recall, and precision and recall on individual class examples. On the other hand maximal itemset based association rules, that are a subset of closed itemset based rules, show to be insufficient in this regard, and typically will have worse recall and generalization power. Empirical results also show the downfall of using the confidence measure at the start to generate association rules, as typically done within the association rule framework. Removing rules that occur below a certain confidence threshold, will also remove the knowledge of existence of any contradictions in the data to the relatively higher confidence rules, and thus precision can be increased by disregarding contradictive rules prior to application of confidence constraint.


Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499 (1994)
Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Conference on Management of Data, Washington, DC, May 16–18, pp. 217–226 (1993)
Agresti, A.: An Introduction to Categorical Data Analysis, 2nd edn. Wiley, New York (2007)
AidIn, T., Güvenir, H.A.: Modeling interestingness of streaming association rules as a benefit-maximizing classification problem. In: Knowledge-Based Systems, vol. 22, pp. 85–99. Elsevier, Amsterdam (2009)
Bay, S.D., Pazzani, M.J.: Detecting group differences: mining contrast sets. Data Min. Knowl. Discov. 5, 213–246 (2001)
Bayardo, R.J.: Efficiently mining long patterns from databases. In: ACM SIGMOD International Conference on Management of Data, pp. 85–93 (1998)
Bayardo, R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. Data Min. Knowl. Discov. 4, 217–240 (2000)
Blanchard, J., Guillet, F., Gras, R., Briand, H.: Using information-theoretic measures to assess association rule interestingness. In: Proceedings of the 5th IEEE International Conference on Data Mining, Houston, Texas, USA, pp. 66–73 (2005)
Brijs, T., Vanhoof, K., Wets, G.: Defining interestingness for association rules. Int. J. Inf. Theories Appl. 10(4), 370–376 (2003)
Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative frequent pattern analysis for effective classification. In: 23rd IEEE International Conference on Data Engineering (ICDE’07), pp. 716–725 (2007)
Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: 24th International Conference on Data Engineering (ICDE’08), pp. 169–178 (2008)
Frank, A., Asuncion, A.: UCI machine learning repository http://archive.ics.uci.edu/ml Irvine, CA: University of California, School of Information and Computer Science (2010)
Garriga, G.C., Kralj, P., Lavrac, N.: Closed sets for labeled data. J. Mach. Learn. Res. 9, 559–580 (2008)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9 (2006)
Goodman, A., Kamath, C., Kumar, V.: Data analysis in the 21st century. Stat. Anal. Data Min. 1(1), 1–3 (2008)
Gouda, K., Zaki, M.J.: Efficiently mining maximal frequent itemsets. In: 1st IEEE International Conference on Data Mining (ICDM’01), pp. 163–170 (2001)
Hadzic, F., Dillon, T.S.: Using the symmetrical tau (τ) criterion for feature selection in decision tree and neural network learning. In: 2nd SIAM Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics (2006)
Hämäläinen, W., Nykänen, M.: Efficient discovery of statistically significant association rules. In: 8th IEEE International Conference on Data Mining, pp. 203–212 (2008)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (1989)
Lallich, S., Teytaud, O., Prudhomme, E.: Association rule interestingness: measure and statistical validation. In: Guillet, F.J., Hamilton, H.J. (eds.) Quality Measures in Data Mining, pp. 251–275. Springer, Berlin (2007)
Lavrac, N., Flach, P., Zupan, B.: Rule evaluation measures: a unifying view. Inductive Log. Program. 174–185 (1999)
Le Bras, Y., Lenca, P., Lallich, S.: Mining classification rules without support: an anti-monotone property of Jaccard measure. In: 14th International Conference on Discovery Science. LNCS, vol. 6926, pp. 179–193. Springer, Berlin (2011)
Le Bras, Y., Lenca, P., Lallich, S.: Formal framework for the study of algorithmic properties of objective interestingness measures. In: Holmes, D.E., Jain, L.C. (eds.) Data Mining: Foundations and Intelligent Paradigms, ISRL, vol. 24, pp. 77–98 (2012)
Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. Eur. J. Oper. Res. 184, 610–626 (2008)
Li, J.: On optimal rule discovery. IEEE TKDD 18(4), 460–471 (2006)
Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: 2001 IEEE International Conference on Data Mining (ICDM’01), pp. 369–376 (2001)
Li, J., Shen, H., Topor, R.W.: Mining the optimal class association rule set. Knowl.-Based Syst. 15, 399–405 (2002)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. Wiley, New York (2002)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Liu, B., Ma, Y., Wong, C.: Improving an association rule based classifier. In: Zighed, D., Komorowski, J., Zytkow, J. (eds.) Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 504–509 (2000)
McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. Rev. 20, 39–61 (2005)
Meggido, N., Srikant, R.: Discovering predictive association rules. In: 4th International Conference on Knowledge Discovery in Databases and Data Mining, pp. 274–278 (1998)
Novak, P.K., Lavrac, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging patterns and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)
Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. Knowl. Discov. Database 229–248 (1991)
Refaat, M.: Data Preparation for Data Mining Using SAS. Morgan Kaufmann, San Francisco (2007)
Shaharanee, I.N.M., Hadzic, F., Dillon, T.S.: Interestingness measures for association rules based on statistical validity. Knowl.-Based Syst. 24, 386–392 (2011)
Silverstein, C., Brin, S., Motwani, R.: Beyond market baskets: generalizing association rules to dependence rules. Data Min. Knowl. Discov. 2, 39–68 (1998)
Simon, G.J., Kumar, V., Li, P.W.: A simple statistical model and association rule filtering for classification. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), pp. 823–831 (2011)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 32–41 (2002)
Veloso, A., Meira, W., Zaki, M.J.: Lazy associative classification. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM’06), pp. 645–654 (2006)
Wang, K., He, Y., Cheung, D.W.: Mining confident rules without support requirement. In: 10th International Conference on Information and Knowledge Management, pp. 89–96 (2001)
Webb, G.I.: Discovering significant patterns. Mach. Learn. 1–33 (2007)
Wei, J.-M., Yi, W.-G., Wang, M.-Y.: Novel measurement for mining effective association rules. Knowl.-Based Syst. 19, 739–743 (2006)
Yin, X., Han, J.: CPAR: classification based on predictive association rules. In: Proceedings of the SIAM International Conference on Data Mining (SDM’03), pp. 369–376 (2003)
Zaki, M.J.: Mining non-redundant association rules. Data Min. Knowl. Discov. 9(3), 223–248 (2004)
Zaki, M.J., Hsiao, C.J.: CHARM: an efficient algorithm for closed itemset mining. In: 2nd SIAM International Conference in Data Mining (2002)
Zhang, C., Zhang, S.: Collecting quality data for database mining. In: AI 2001: Advances in Artificial Intelligence, pp. 131–142 (2001)
Zhou, X.J., Dillon, T.S.: A statistical-heuristic feature selection criterion for decision tree induction. IEEE Trans. Pattern Anal. Mach. Intell. 13, 834–841 (1991)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shaharanee, I.N.M., Hadzic, F. Evaluation and optimization of frequent, closed and maximal association rule based classification. Stat Comput 24, 821–843 (2014). https://doi.org/10.1007/s11222-013-9404-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11222-013-9404-6