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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, L., Zhao, Y., Zhang, H., et al.: Flexible frameworks for actionable knowledge discovery. J. Knowl. Data Eng. 22(9), 1299–1312 (2010)
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)
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)
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)
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)
Minaei-Bidgoli, B., Barmaki, R., Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms. J. Inf. Sci. 233, 15–24 (2013)
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)
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)
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)
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)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. J. Inf. Sci. 179(13), 2232–2248 (2009)
Van den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. J. Inf. Sci. 176(8), 937–971 (2006)
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)
Zhan, Z.H., Zhang, J., Li, Y., et al.: Adaptive particle swarm optimization. J. Syst. Man Cybern. 39(6), 1362–1381 (2009)
Sarath, K., Ravi, V.: Association rule mining using binary particle swarm optimization. J. Eng. Appl. Artif. Intell. 26(8), 1832–1840 (2013)
Acknowledgement
This work is supported by Jiangsu Province Joint Research Project Foundation(BY2013015-33) and Nature Science Foundation of Jiangsu Province(NO. BK20131107)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, Z., Zhang, D., Sun, Z., Wang, J. (2015). An Adaptive Hybrid PSO and GSA Algorithm for Association Rules Mining. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-27051-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27050-0
Online ISBN: 978-3-319-27051-7
eBook Packages: Computer ScienceComputer Science (R0)