Abstract
Data mining is a blooming area in information science. Mining association rules aims to find the relationship among items in the databases and has become one of the most important data mining technologies. Previous study shows the capability of genetic algorithm (GA) to find the membership functions for fuzzy data mining. However, the chromosome representation cannot avoid the occurrence of inappropriate arrangement of membership functions, resulting in inefficiency of GA in searching for the optimal membership functions. This study proposes a novel representation that takes advantage of the structure information of membership functions to deal with the issue. In the light of overlap and coverage, we propose two heuristics for appropriate arrangement of membership functions. The experimental results show that GA using the proposed representation can achieve high fitness and suitability. The results also indicate that the two heuristics help to well exploit the structure information and therefore enhance GA in terms of solution quality and convergence speed on fuzzy association rules mining.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Procddings of 20th International Conference on Very Large Data Bases (VLDB), pp. 487–499. Morgan Kaufmann (1994)
Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(27), 1–27 (2011)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. In: Natural Computing. Springer, Berlin (2003)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine 17, 37–54 (1996)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: Advances in knowledge discovery and data mining. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) From Data Mining to Knowledge Discovery: An Overview, pp. 1–34. American Association for Artificial Intelligence (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Herrera, F., Lozano, M., Verdegay, J.L.: Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets System 92(1), 21–30 (1997)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Hong, T.-P., Chen, C.-H., Lee, Y.-C., Wu, Y.-L.: Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Transaction on Evolutionary Computation 12(2), 252–265 (2008)
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 Computing 10(11), 1091–1101 (2006)
Hong, T.-P., Kuo, C.-S., Chi, S.-C.: Mining association rules from quantitative data. Intelligent Data Analysis 3(5), 363–376 (1999)
**, Y.: Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation 1, 61–70 (2011)
Piatesky-Shapiro, G., Brachman, R., Klösgen, W., Simoudis, E.: An overview of issues in develo** industrial data mining and knowledge discovery applications. In: Proceedings of Knowledge Discovering and Data Mining, pp. 89–95 (1996)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. SIGMOD Record (ACM Special Interest Group on Management of Data) 25(2), 1–12 (1996)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means clustering with background knowledge. In: Proceedings 18th International Conference on Machine Learning, pp. 577–584 (2001)
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Ting, CK., Wang, TC., Liaw, RT. (2015). An Efficient Representation for Genetic-Fuzzy Mining of Association Rules. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_47
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DOI: https://doi.org/10.1007/978-3-319-13356-0_47
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