Abstract
Association rule mining problem attracts the attention of researchers inasmuch to its importance and applications in our world with the fast growth of the stored data. Association rule mining process is computationally very expensive because rules number grows exponentially as items number in the database increases. However, Association rule mining is more complex when we introduce the quality criteria and usefulness to the user. This paper deals with association rule mining issue in which we propose Multi-Objective Bat algorithm for association rules mining Known as MOB-ARM. With the aim of extract more useful and understandable rules. We introduce four quality measures of association rules: Support, Confidence, Comprehensibility, and Interestingness in two objective functions considered for maximization. A series of experiments are carried out on several well-known benchmarks in association rule mining field and the performance of our proposal are evaluated and compared with those of other recently published methods including mono-objective and multi-objective approaches. The outcomes show a clear superiority of our proposal in-face-of mono objective methods in terms generated rules number and rule quality. Also, The analysis also shows a competitive outcomes in terms of quality against multi-objective optimization methods.
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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)
Alatas, B., Akin, E., Karci, A.: Modenar: multi-objective differential evolution algorithm for mining numeric association rules. Appl. Soft Comput. 8(1), 646–656 (2008)
Angiulli, F., Ianni, G., Palopoli, L.: On the complexity of mining association rules. In: SEBD, pp. 177–184 (2001)
Beiranvand, V., Mobasher-Kashani, M., Bakar, A.A.: Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. Expert Syst. Appl. 41(9), 4259–4273 (2014)
Coello, C.A., Coello, D.A., Veldhuizen, V., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems, vol. 242. Springer, Heidelberg (2002)
Djenouri, Y., Drias, H., Habbas, Z., Mosteghanemi, H.: Bees swarm optimization for web association rule mining. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 3, pp. 142–146. IEEE (2012)
Djenouri, Y., Drias, H., Habbas, Z.: Bees swarm optimisation using multiple strategies for association rule mining. Int. J. Bio-Inspired Comput. 6(4), 239–249 (2014)
Ganghishetti, P., Vadlamani, R.: Association rule mining via evolutionary multi-objective optimization. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds.) MIWAI 2014. LNCS (LNAI), vol. 8875, pp. 35–46. Springer, Cham (2014). doi:10.1007/978-3-319-13365-2_4
Goethls, B., Zaki, M.J.: Frequent itemset mining dataset repository (2003). http://fimi.ua.ac.be/data/
Guvenir, H.A., Uysal, I.: Bilkent university function approximation repository (2000). http://funapp.cs.bilkent.edu.tr/DataSets/
Heraguemi, K.E., Kamel, N., Drias, H.: Association rule mining based on bat algorithm. J. Comput. Theor. Nanosci. 12(7), 1195–1200 (2015)
Heraguemi, K.E., Kamel, N., Drias, H.: Multi-population cooperative bat algorithm for association rule mining. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9329, pp. 265–274. Springer, Cham (2015). doi:10.1007/978-3-319-24069-5_25
Heraguemi, K.E., Kamel, N., Drias, H.: Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies. Appl. Intell. 45(4), 1021–1033 (2016). Springer
Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z.: A new evolutionary algorithm for extracting a reduced set of interesting association rules. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 133–142. Springer, Cham (2015). doi:10.1007/978-3-319-26535-3_16
Minaei-Bidgoli, B., Barmaki, R., Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms. Inf. Sci. 233, 15–24 (2013)
Al-maqaleh, M.B.: Discovering interesting association rules: a multi-objective genetic algorithm approach. Int. J. Appl. Inf. Syst. 5(3), 47–52 (2013)
Olmo, J.L., Luna, J.M., Romero, J.R., Ventura, S.: Association rule mining using a multi-objective grammar-based ant programming algorithm. In: 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 971–977, November (2011)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12538-6_6
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Heraguemi, K.E., Kamel, N., Drias, H. (2017). Multi-objective Bat Algorithm for Mining Interesting Association Rules. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_2
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DOI: https://doi.org/10.1007/978-3-319-58130-9_2
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