A genetic algorithm-based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Document Type : Article


School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran


Discovering association rules is a useful and common technique for data mining in which relations and co-dependencies between datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the Genetic Algorithm, and there is no need there is no need to determine the extent of the threshold for the support and confidence criteria. By proposing a multi-criteria method, the useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discrete numerical values and the determination of the minimum support threshold and minimum confidence threshold. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness, and cosine2. The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets and the results show the performance superiority of the proposed algorithm compared to similar algorithms.


Main Subjects

1. Agrawal, R., Imieli_nski, T., and Swami, A. Mining association rules between sets of items in large databases", In ACM SIGMOD Record, 22(2), pp. 207{ 216 (June 1993). 2. Haeri, A. and Tavakkoli-Moghaddam, R. Developing a hybrid data mining approach based on multiobjective particle swarm optimization for solving a traveling salesman problem", Journal of Business Economics and Management, 13(5), pp. 951{967 (2012). 3. Soysal, O.M. Association rule mining with mostly associated sequential patterns", Expert Systems with Applications, 42(5), pp. 2582{2592 (2015). 4. Rana, M. and Mann, P.S. Analysis of MFGA to extract interesting rules" International Journal of Computer Applications, 84(3), pp. 15{21 (2013). 5. _Alvarez, V.P. and V_azquez, J.M. An evolutionary algorithm to discover quantitative association rules 1330 F. Moslehi and A. Haeri/Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 1316{1332 from huge databases without the need for an a priori discretization", Expert Systems with Applications, 39(1), pp. 585{593 (2012). 6. Miller, R.J. and Yang, Y. Association rules over interval data", ACM SIGMOD Record, 26(2), pp. 452{ 461 (1997). 7. Moslehi, P., Bidgoli, B.M., Nasiri, M., and Salajegheh, A. Multi-objective numeric association rules mining via ant colony optimization for continuous domains without specifying minimum support and minimum con_dence", International Journal of Computer Science Issues (IJCSI), 8(5), pp. 1{34 (2011). 8. Ehrgott, M., Multicriteria Optimization, 491. Springer Science & Business Media (2005). 9. Coello, C.A.C., Lamont, G.B., and Van Veldhuizen, D.A., Evolutionary Algorithms for Solving Multi- Objective Problems, Springer, 800 (2002). 10. Coello, C.A.C., Lamont, G.B., and Van Veldhuizen, D.A., Evolutionary Algorithms for Solving Multi- Objective Problems, 5, New York: Springer (2007). 11. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, 16, John Wiley & Sons (2001). 12. Kaya, M. and Alhajj, R. Genetic algorithm based framework for mining fuzzy association rules", Fuzzy Sets and Systems, 152(3), pp. 587{601 (2005). 13. Kaya, M. and Alhajj, R. Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining", Applied Intelligence, 24(1), pp. 7{15 (2006). 14. Alatas, B. and Akin, E. Rough particle swarm optimization and its applications in data mining", Soft Computing, 12(12), pp. 1205{1218 (2008). 15. Ayubi, S., Muyeba, M.K., Baraani, A., et al. An algorithm to mine general association rules from tabular data", Information Sciences, 179(20), pp. 3520{3539 (2009). 16. Nasiri, M., Taghavi, L.S., and Minaee, B. Multiobjective rule mining using simulated annealing algorithm", Journal of Convergence Information Technology, 5(1), pp. 60{68 (2010). 17. Qodmanan, H.R., Nasiri, M., and Minaei-Bidgoli, B. Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum con_dence", Expert Systems with Applications, 38(1), pp. 288{298 (2011). 18. Djenouri, Y., Drias, H., and Chemchem, A. A hybrid bees swarm optimization and tabu search algorithm for association rule mining", In Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on IEEE pp. 120{125, (Aug, 2013). 19. Agbehadji, I.E., Fong, S., and Millham, R. Wolf search algorithm for numeric association rule mining", In Cloud Computing and Big Data Analysis (ICCCBDA), 2016 IEEE International Conference on IEEE pp. 146{151 (July, 2016). 20. Can, U. and Alatas, B. Automatic mining of quantitative association rules with gravitational search algorithm", International Journal of Software Engineering and Knowledge Engineering, 27(3), pp. 343{ 372 (2017). 21. Erickson, M., Mayer, A., and Horn, J. The niched Pareto genetic algorithm 2 applied to the design of groundwater remediation systems", In International Conference on Evolutionary Multi-Criterion Optimization, (pp. 681{695), Springer, Berlin, Heidelberg (March, 2001). 22. Alata_s, B. and Akin, E. An e_cient genetic algorithm for automated mining of both positive and negative quantitative association rules", Soft Computing, 10(3), pp. 230{237 (2006). 23. Alcala-Fdez, J., Flugy-Pape, N., Bonarini, A., and Herrera, F. Analysis of the e_ectiveness of the genetic algorithms based on extraction of association rules", Fundamental Informaticae, 98(1), pp. 1{14 (2010). 24. Mata, J., Alvarez, J.L., and Riquelme, J.C. Mining numeric association rules with genetic algorithms", In Arti_cial Neural Nets and Genetic Algorithms, pp. 264{267, Springer, Vienna (2001). 25. Mata, J., Alvarez, J.L., and Riquelme, J.C. Discovering numeric association rules via evolutionary algorithm", Advances in Knowledge Discovery and Data Mining, pp. 40{51 (2002). 26. Salleb-Aouissi, A., Vrain, C., and Nortet, C. Quant- Miner: A genetic algorithm for mining quantitative association rules", In IJCAI, 7, pp. 1035{1040 (2007, January). 27. Taboada, K., Gonzales, E., Shimada, K., et al. Association rule mining for continuous attributes using genetic network programming", IEEJ Transactions on Electrical and Electronic Engineering, 3(2), pp. 199{ 211 (2008). 28. Yan, X., Zhang, C., and Zhang, S. Genetic algorithmbased strategy for identifying association rules without specifying actual minimum support", Expert Systems with Applications, 36(2), pp. 3066{3076 (2009). 29. Mart__nez-Ballesteros, M., Mart__nez- _Alvarez, F., Troncoso, A., and Riquelme, J.C. An evolutionary algorithm to discover quantitative association rules in multidimensional time series", Soft Computing, 15(10), p. 2065 (2011). 30. Mart__n, D., Rosete, A., Alcal_a-Fdez, J., and Herrera, F. A multi-objective evolutionary algorithm for mining quantitative association rules", In Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, pp. 1397{1402, IEEE (November, 2011). 31. Minaei-Bidgoli, B., Barmaki, R., and Nasiri, M. Mining numerical association rules via multi-objective genetic algorithms", Information Sciences, 233, pp. 15{24 (2013). F. Moslehi and A. Haeri/Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 1316{1332 1331 32. Mart__n, D., Rosete, A., Alcal_a-Fdez, J., and Herrera, F. QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules", Information Sciences, 258, pp. 1{28 (2014). 33. Mart__n, D., Alcal_a-Fdez, J., Rosete, A., and Herrera, F. NICGAR: A niching genetic algorithm to mine a diverse set of interesting quantitative association rules", Information Sciences, 355, pp. 208{228 (2016). 34. Indira, K. and Kanmani, S. Mining association rules using hybrid genetic algorithm and particle swarm optimisation algorithm", International Journal of Data Analysis Techniques and Strategies, 7(1), pp. 59{76 (2015). 35. Agarwal, A. and Nanavati, N. Association rule mining using hybrid GA-PSO for multi-objective optimisation", In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pp. 1{7, IEEE (December, 2016). 36. Sarkar, S., Lohani, A., and Maiti, J. Genetic algorithm-based association rule mining approach towards rule generation of occupational accidents", In International Conference on Computational Intelligence, Communications, and Business Analytics, pp. 517{530, Springer, Singapore (March, 2017). 37. Djenouri, Y., Belhadi, A., Fournier-Viger, P., and Fujita, H. Mining diversi_ed association rules in big datasets: A cluster/GPU/genetic approach", Information Sciences, 459, pp. 117{134 (2018). 38. Kumar, P. and Singh, A.K. E_cient generation of association rules from numeric data using genetic algorithm for smart cities", In Security in Smart Cities: Models, Applications, and Challenges pp. 323{ 343, Springer, Cham. (2019). 39. Mart__nez-Ballesteros, M., Bacardit, J., Troncoso, A., and Riquelme, J.C. Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets", Integrated Computer- Aided Engineering, 22(1), pp. 21{39 (2015). 40. Padillo, F., Luna, J.M., Herrera, F., et al. Mining association rules on big data through MapReduce genetic programming", Integrated Computer-Aided Engineering, 25(1), pp. 31{48 (2018). 41. Mart__n, D., Mart__nez-Ballesteros, M., Garc__a-Gil, D., et al. MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems", Knowledge-Based Systems, 153, pp. 176{ 192 (2018). 42. Haery, A., Salmasi, N., Yazdi, M.M., and Iranmanesh, H. Application of association rule mining in supplier selection criteria", World Academy of Science, Engineering and Technology, 40(1), pp. 358{362 (2008). 43. Srikant, R. and Agrawal, R. Mining quantitative association rules in large relational tables", In AcmSigmod Record, 25(2), pp. 1{12, ACM (1996, June). 44. Shenoy, P.D., Srinivasa, K.G., Venugopal, K.R., and Patnaik, L.M. Dynamic association rule mining using genetic algorithms", Intelligent Data Analysis, 9(5), pp. 439{453 (2005). 45. Kuo, R.J., Chao, C.M., and Chiu, Y.T. Application of particle swarm optimization to association rule mining", Applied Soft Computing, 11(1), pp. 326{336 (2011). 46. Beiranvand, V., Mobasher-Kashani, M., and Bakar, A.A. Multi-objective PSO algorithm for mining numerical association rules without a priori discretization", Expert Systems with Applications, 41(9), pp. 4259{4273 (2014). 47. Freitas, A.A., Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer (2002). 48. Ghosh, A. and Nath, B. Multi-objective rule mining using genetic algorithms", Information Sciences, 163, pp. 123{133 (2004). 49. Prajapati, D.J., Garg, S., and Chauhan, N.C. Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment", Future Computing and Informatics Journal, 2(1), pp. 19{30 (2017). 50. Merceron, A. and Yacef, K. Interestingness measures for association rules in educational data", EDM, 8, pp. 57{66 (2008). 51. Rokh, B., Mirvaziri, H., and Eftekhari, M. Proposing an e_cient combination of interesting measures for mining association rules via NSGA-II", In Technology, Communication and Knowledge (ICTCK), 2014 International Congress on, pp. 1{7, IEEE (November, 2014). 52. Luna, J.M., Romero, J.R., and Ventura, S. Grammarbased multi-objective algorithms for mining association rules", Data & Knowledge Engineering, 86, pp. 19{37 (2013). 53. Hsieh, Y., Lee, P., and You, P. Immune based evolutionary algorithm for determining the optimal sequence of multiple disinfection operations", Scientia Iranica, 26(2), pp. 959{974 (2019). DOI: 10.24200/sci.2018.20324 54. Sadeghi, H., Zolfaghari, M., and Heydarizade, M. Estimation of electricity demand in residential sector using genetic algorithm approach", Journal of Industrial Engineering & Production Research, 22(1), pp. 43{50 (2011). 55. Ostadi, B., MotamediSedeh, O., Husseinzadeh Kashan, A., and Amin-Naseri, M. An intelligent model to predict the day-ahead deregulated market clearing price: a hybrid NN, PSO and GA approach", Scientia Iranica, 26(6), pp. 3846{3856 (2019). DOI: 10.24200/sci.2018.50910.1909 56. Russell, S.J. and Norvig, P., Arti_cial Intelligence A Modern Approach, Pearson Education (2008). 1332 F. Moslehi and A. Haeri/Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 1316{1332 57. Goldberg, D.E., Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley, p. 41 (1989). 58. Sonagara, D. and Badheka, S. Comparison of basic clustering algorithms", Int. J. Comput. Sci. Mob. Comput, 3(10), pp. 58{61 (2014). 59. Dabbagh, H., GhodratiAmiri, G., and Shaabani, S. Modal data-based approach to structural damage identi_cation by means of imperialist competitive optimization algorithm", Scientia Iranica, 25(3), pp. 1070{1080 (2018). DOI: 10.24200/sci.2017.4590 60. Mart__nez-Ballesteros, M. and Riquelme, J.C. Analysis of measures of quantitative association rules", In International Conference on Hybrid Arti_cial Intelligence Systems, pp. 319{326, Springer, Berlin, Heidelberg (May, 2011). 61. Picek, S. and Golub, M. Comparison of a crossover operator in binary-coded genetic algorithms", WSEAS Transactions on Computers, 9, pp. 1064{1073 (2010). 62. Guvenir, H. and Uysal, I., Bilkent University Function Approximation Repository (2000). <http://funapp.cs.bilkent.edu.tr/> 63. Moslehi, F., Haeri, A., and Moini, A. Analyzing and investigating the use of electronic payment tools in Iran using data mining techniques", Journal of AI and Data Mining, 6(2), pp. 417{437 (2018). DOI: 10.22044/jadm.2017.5352.1643