Abstract
This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.
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References
Aggarwal C, Yu P (2010) On clustering massive text and categorical data streams. Knowl Inf Syst 24: 171–196
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: VLDB’94. Proceedings of 20th international conference on very large data bases, Santiago de Chile, Chile, pp 487–499
Alatas B, Akin E (2006) An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Comput 10: 230–237
Borgelt C (2003) Efficient implementations of Apriori and Eclat. In: FIMI’03. 1st Workshop on frequent itemset mining implementations, Melbourne, Florida, USA
Chi Y, Wang H, Yu PS, Muntz RR (2006) Catch the momento: maintaining closed frequent itemsets over a data stream sliding window. Knowl Inf Syst 10(3): 265–294
Coenen F, Goulbourne G, Leng P (2000) Algorithms for computing association rules using a partial- support tree. Knowl-Based Syst 13: 141–149
Coenen F, Goulbourne G, Leng P (2003) Tree structures for mining association rules. Data Min Knowl Discov 8(1): 25–51
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7: 1–30
Do TD, Hui SC, Fong ACM (2004) Mining association rules using relative confidence. In: IDEAL 2004, Proceedings of the 5th international conference on intelligent data engineering and automated learning, Exeter, UK, vol. 3177 of lecture notes in computer science, pp 306–313
Domeniconi F, Tagarelli C, Gullo A (2009) Projective clustering ensembles. In: ICDM ’09, Proceedings of the IEEE international conference on data mining, Miami, USA, pp 794–799
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, New York
Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern C 40(2): 121–144
Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, Heidelberg
Fukuda T, Morimoto Y, Morishita S, Tokuyama T (1996) Mining optimized association rules for numeric attributes. In: Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, Montreal, Canada, ACM Press
García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behavior: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6): 617–644
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co, Boston
Guan JW, Bell DA, Liu D (1989) Data mining for maximal frequent patterns in sequence groups. vol. 5 of studies in computational intelligence. Springer, New York, pp pp 137–161
Hájek P, Havel I, Chytil M (1966) The GUHA method of automatic hypotheses determination. Comput 1(4): 293–308
Han J, Kamber M (2006) Data mining. Concepts and techniques. The Morgan Kaufmann series in data management systems, 2nd edition. Morgan Kaufmann, Burlington
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: SIGMOD 2000, Proceedings of the 2000 ACM SIGMOD international conference on management of data, Dallas, Texas, USA, pp 1–12
Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 8: 53–87
Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge
Kianmehr K, Alshalalfa M, Alhajj R (2010) Fuzzy clustering-based discretization for gene expression classification. Knowl Inf Syst 24: 441–465
Koknar-Tezel S, Latecki L (2010) Improving SVM classification on imbalanced time series data sets with ghost points. Knowl Inf Syst, pp 1–23. http://dx.doi.org/10.1007/s10115-010-0310-3
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection (complex adaptive systems). The MIT Press, Cambridge
Koza JR (2008) Introduction to genetic programming: tutorial. In: GECCO’08, Proceedings of the 10th annual conference on genetic and evolutionary computation Atlanta, Georgia, USA, ACM, pp 2299–2338
Lai PL, Chiu R, Hsu CC, Hsu CI (2003) The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance. Expert Syst Appl 25(1): 51–62
Liu H, Hussain F, Tan CL, Dash M (2002) Discretization: an enabling technique. Data Min Knowl Discov 6: 2393–2423
Martin L, Leblanc R, Toan NK (1993) Tables for the Friedman rank test. Can J Stat 21(1): 39–43
Mata J, Alvarez JL, Riquelme JC (2002) Discovering numeric association rules via evolutionary algorithm. vol. 2336/2002 of lecture notes in computer science
Mata J, Alvarez JL, Riquelme JC (2001) Mining numeric association rules via evolutionary algorithm. In: ICANNGA’01, Proceedings of the 5th international conference on artificial neural networks and genetic algorithms, Prague, Czech Republic, pp 264–267
McKay R, Hoai N, Whigham P, Shan Y, ONeill M (2010) Grammar-based genetic programming: a survey. Genet Program Evolvable Mach 11: 365–396
Ordonez C, Ezquerra N, Santana CA (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst 9(3): 259–283
Ratle A, Sebag M (2000) Genetic programming and domain knowledge: beyond the limitations of grammar-guided machine discovery. In: PPSN VI. Proceedings of the 6th international conference on parallel problem solving from nature, Paris, France, pp 211–220
Rauch J (2005) Logic of association rules. Appl Intell 22(1): 9–28
Rodríguez-González A, Martínez-Trinidad J, Carrasco-Ochoa J, Ruiz-Shulcloper J (2010) RP-Miner: a relaxed prune algorithm for frequent similar pattern mining. Knowl Inf Syst 27(3): 1–21
Salleb-Aouissi A, Vrain C, Nortet C (2007) QuantMiner: a genetic algorithm for mining quantitative association rules. In: IJCAI’97, Proceedings of the 20th international joint conference on artificial intelligence, Hyberadad, India, pp 1035–1040
Schuster A, Wolff R, Trock D (2004) A high-performance distributed algorithm for mining association rules. Knowl Inf Syst 7(4): 458–475
Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: SIGMOD’96, Proceedings of the 1996 ACM SIGMOD international conference on management of data, Montreal, Quebec, Canada
Sucahyo YG, Gopalan RP (2004) Building a more accurate classifier based on strong frequent patterns. In: AJCAI 2004, Proceedings of the 17th Australian joint conference on artificial intelligence, Cairns, Australia, vol. 3339, pp 1036–1042
Tsymbal A, Pechenizkiy M, Cunningham P (2005) Sequential genetic search for ensemble feature selection. In: IJCAI 2005, Proceedings of the 19th international joint conference on artificial intelligence, Edinburgh, Scotland, pp 877–882
Ventura S, Romero C, Zafra A, Delgado JA, Hervás C (2007) JCLEC: a framework for evolutionary computation. vol.12 of soft computing. Springer, Heidelberg
Yan X, Zhang C, Zhang S (2005) ARMGA: identifying interesting association rules with genetic algorithms. Appl Artif Intell 19(7): 677–689
Yan X, Zhang D, Zhang S (2009) Algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst Appl 36(2): 3066–3076
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Luna, J.M., Romero, J.R. & Ventura, S. Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules. Knowl Inf Syst 32, 53–76 (2012). https://doi.org/10.1007/s10115-011-0419-z
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DOI: https://doi.org/10.1007/s10115-011-0419-z