Abstract
This paper adopts a co-evolution algorithm, which utilizes improved genetic algorithm and particle swarm optimization algorithm to iterate two populations simultaneously. Meanwhile, the mechanism of information interaction between these two populations is introduced. Finally, experiments and application have been made to prove that on the premise of acceptable time complexity, not only does the co-evolution algorithm inherit the superiority of traditional genetic algorithm such as reducing the number of scanning the database effectively and generating small-scale candidate item sets, but also avoid the phenomenon of premature through comparing the properties of co-evolution algorithm, traditional genetic algorithm and particle swarm optimization algorithm when used in association rules mining. High quality association rules can be found when adopted the co-evolution algorithm, especially in high-dimension database.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn., pp. 146–155. China Machine Press (2011)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. 1993 ACM-SIGMOD Int. Conf. Management of Databases, pp. 20–72. ACM Press, Washington, DC (1993)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data, Dallas, Texas, pp. 1–12. ACM Press (2000)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) Proceedings of the 21th International Conference on Very Large Databases(VLDB 1995), Zurich, pp. 432–443. Morgan Kaufmann Publisher (1995)
Wiegand, R.P.: An analysis of cooperative co-evolutionary algorithms. George Mason University, Fairfax (2003)
Sharma, S.K., Irwin, G.W.: Fuzzy coding of genetic algorithms. IEEE Trans. Evolutionary Computation, 344–355 (2003)
Thierens, D.: Adaptive mutation rate control schemes in genetic algorithms. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 980–985 (2002)
Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Adaptation. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
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Lou, W., Zhu, L., Yan, L. (2012). The Research on Association Rules Mining with Co-evolution Algorithm in High Dimensional Data. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34390-2_38
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DOI: https://doi.org/10.1007/978-3-642-34390-2_38
Publisher Name: Springer, Berlin, Heidelberg
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