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An Adaptive Hybrid PSO and GSA Algorithm for Association Rules Mining

  • Zhiping ZhouEmail author
  • Daowen Zhang
  • Ziwen Sun
  • Jiefeng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9483)

Abstract

Association rule mining is an interesting topic to extract hidden knowledge in data mining. Particle Swarm Optimization(PSO) has been used to mine Association rules, but it suffers from easily falling into local optimum. Gravitational search algorithm(GSA) has high performance in searching the global optimum but it suffers from running slowly especially in the last iterations. In order to resolve the aforementioned problem, in this paper a new hybrid algorithm called A_PSOGSA is proposed for association rules mining. Firstly, it integrates PSO and GSA. To make the idea simpler, PSO will browse the search space in such away to cover most of its regions and the local exploration of each particle is computed by GSA search. Secondly, the acceleration coefficients are controlled dynamically with the population distribution information during the process of evolution in order to provide a better balance between the ability of global and local searching. The experiments verify the accuracy and the effectiveness of the algorithm in this paper compared with the other algorithms for mining association rules.

Keywords

Association rules mining Particle Swarm Optimization Gravitational Search Algorithm Dynamically updating approach 

Notes

Acknowledgement

This work is supported by Jiangsu Province Joint Research Project Foundation(BY2013015-33) and Nature Science Foundation of Jiangsu Province(NO. BK20131107)

References

  1. 1.
    Cao, L., Zhao, Y., Zhang, H., et al.: Flexible frameworks for actionable knowledge discovery. J. Knowl. Data Eng. 22(9), 1299–1312 (2010)CrossRefGoogle Scholar
  2. 2.
    Lan, G.C., Hong, T.P., Tseng, V.S.: A projection-based approach for discovering high average-utility itemsets. J. Inf. Sci. Eng. 28(1), 193–209 (2012)Google Scholar
  3. 3.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM, New York (2000)Google Scholar
  4. 4.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM, New York (1993)Google Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)Google Scholar
  6. 6.
    Minaei-Bidgoli, B., Barmaki, R., Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms. J. Inf. Sci. 233, 15–24 (2013)CrossRefGoogle Scholar
  7. 7.
    Beiranvand, V., Mobasher-Kashani, M., Bakar, A.A.: Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. J. Expert Syst. Appl. 41(9), 4259–4273 (2014)CrossRefGoogle Scholar
  8. 8.
    Sundaramoorthy, S., Shantharajah, S.P.: An improved ant colony algorithm for effective mining of frequent items. J. Web Eng. 13(3–4), 263–276 (2014)Google Scholar
  9. 9.
    Agrawal, J., Agrawal, S., Singhai, A., et al.: SET-PSO-based approach for mining positive and negative association rules. J. Knowl. Inf. Syst., 1–19 (2014)Google Scholar
  10. 10.
    Kaur, S., Goyal, M.: Fast and robust hybrid particle swarm optimization tabu search association rule mining (HPSO-ARM) algorithm for web data association rule mining (WDARM). J. Adv. Res. Comput. Sci. Manag. Stud. 2, 448–451 (2014)Google Scholar
  11. 11.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. J. Inf. Sci. 179(13), 2232–2248 (2009)zbMATHCrossRefGoogle Scholar
  12. 12.
    Van den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. J. Inf. Sci. 176(8), 937–971 (2006)zbMATHCrossRefGoogle Scholar
  13. 13.
    Mohammadi-Ivatloo, B., Moradi-Dalvand, M., Rabiee, A.: Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. J. Electr. Power Syst. Res. 95, 9–18 (2013)CrossRefGoogle Scholar
  14. 14.
    Zhan, Z.H., Zhang, J., Li, Y., et al.: Adaptive particle swarm optimization. J. Syst. Man Cybern. 39(6), 1362–1381 (2009)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Sarath, K., Ravi, V.: Association rule mining using binary particle swarm optimization. J. Eng. Appl. Artif. Intell. 26(8), 1832–1840 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhiping Zhou
    • 1
    Email author
  • Daowen Zhang
    • 1
  • Ziwen Sun
    • 1
  • Jiefeng Wang
    • 1
  1. 1.School of Internet of Things EngineeringJiangnan UniversityWuxiChina

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