Abstract
In numerous studies on fuzzy association rules mining, membership functions are usually provided by experts. It is unrealistic to predefine appropriate membership functions for every different dataset in real-world applications. In order to solve the problem, metaheuristic algorithms are applied to the membership functions optimization. As a popular metaheuristic method, bat algorithm has been successfully applied to many optimization problems. Thus a novel fuzzy decimal bat algorithm for association rules mining is proposed to automatically extract membership functions from quantitative data. This algorithm has enhanced local and global search capacity. In addition, a new fitness function is proposed to evaluate membership functions. The function takes more factors into account, thus can assess the number of obtained association rules more accurately. Proposed algorithm is compared with several commonly used metaheuristic methods. Experimental results show that the proposed algorithm has better performance, and the new fitness function can evaluate the quality of membership functions more reasonably.
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References
Hong, T.P., Chen, C.H., Wu, Y.L., Lee, Y.C.: A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft. Comput. 10(11), 1091–1101 (2006)
Hong, T.P., Lee, Y.C., Wu, M.T.: An effective parallel approach for genetic-fuzzy data mining. Expert Syst. Appl. 41(2), 655–662 (2014)
Palacios, A.M., Palacios, J.L., Sánchez, L., Alcalá-Fdez, J.: Genetic learning of the membership functions for mining fuzzy association rules from low quality data. Inf. Sci. 295, 358–378 (2015)
Matthews, S.G., Gongora, M.A., Hopgood, A.A.: Evolutionary algorithms and fuzzy sets for discovering temporal rules. Int. J. Appl. Math. Comput. Sci. 23(4), 855–868 (2013)
Matthews, S.G., Gongora, M.A., Hopgood, A.A., Ahmadi, S.: Web usage mining with evolutionary extraction of temporal fuzzy association rules. Knowl.-Based Syst. 54, 66–72 (2013)
Wu, M.T., Hong, T.P., Lee, C.N.: A continuous ant colony system framework for fuzzy data mining. Soft. Comput. 16(12), 2071–2082 (2012)
Alikhademi, F., Zainudin, S.: Generating of derivative membership functions for fuzzy association rule mining by Particle Swarm Optimization. In: 2014 International Conference on Computational Science and Technology (ICCST), pp. 1–6. IEEE (2014)
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
Pérez, J., Valdez, F., Castillo, O.: Modification of the bat algorithm using fuzzy logic for dynamical parameter adaptation. In: IEEE Congress on Evolutionary Computation (CEC) 2015, pp. 464–471. IEEE (2015)
Song, A., Ding, X., Chen, J., Li, M.: Multi-objective association rule mining with binary bat algorithm. Intell. Data Anal. 20(1), 105–128 (2016)
Yilmaz, S., Kucuksille, E.U.: Improved bat algorithm (IBA) on continuous optimization problems. Lect. Notes Softw. Eng. 1(3), 279 (2013)
Yilmaz, S., Kucuksille, E.U.: A new modification approach on bat algorithm for solving optimization problems. Appl. Soft Comput. 28, 259–275 (2015)
Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. inform. 1(4), 355–366 (2006)
UC Irvine Machine Learning Repository. http://archive.ics.uci.edu/ml/
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Song, A., Song, J., Ding, X., Xu, G., Chen, J. (2017). Utilizing Bat Algorithm to Optimize Membership Functions for Fuzzy Association Rules Mining. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_37
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DOI: https://doi.org/10.1007/978-3-319-64468-4_37
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