An effective algorithm for mining association rules based on imperialist competitive algorithm

2011 6th International Conference on Digital Information Management, ICDIM 2011

Volumn , Issue , 2011, Pages 6-11

  Khademolghorani, Fariba ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Association Rules; Evolutionary Algorithm; Imperialist Competitive Algorithm

Indexed keywords

COMPETITIVE ALGORITHMS; EFFECTIVE ALGORITHMS; EFFICIENT ALGORITHM; HIGH QUALITY; ITEM SETS; MINIMUM CONFIDENCE THRESHOLDS; MINIMUM SUPPORT; MINING ASSOCIATIONS; RULES BASED; TWO STAGE;

ASSOCIATION RULES; DATA MINING; EVOLUTIONARY ALGORITHMS; INFORMATION MANAGEMENT;

ALGORITHMS;

EID: 83755162647     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDIM.2011.6093350     Document Type: Conference Paper

References (11)

1. R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases" in: Proceedings of ACM SIGMOD Conference on Management of Data, Washington, Vol. 22, No. 2, 1993, pp. 207-216
2. E. Atashpaz-Gargari, and C. Lucas, "Imperialist Competitive Algorithm An Algorithm for Optimization Inspired by Imperialistic Competition", IEEE Cogress on Evolutionary Compution, Singapore, Jun. 2007, pp. 4661-4667.
3. O. M. Badawy, M. I. Habib, and A. A. Sallam, "Quantitative Association Rule Mining Using a Hybrid PSO/ACO Algorithm (PSO/ACO-AR) ", ACIT'2008, Hammamet, Tunisia, Dec. 2008, pp. 1-9.
4. S. Brin and R. Motwani and J. D. Ullman and S. Tsur, "Dynamic itemset counting and implication rules for market basket data," Proc. of ACM SIGMOD International Conf. on Management of Data. Tucson, Vol. 26, No. 2, Jun. 1997, pp. 255-264.
5. A. Ghosh, and B. Nath, "Muti Objective Association Rule Mining Using Genetic Algorithm", Elsevier Information Sciences, Vol. 163, No. 1, 2004, pp. 123-133.
6. J. Han, and M. Kamber, "Data Mining: Concepts and Techniques" (2nd ed.), Elsevier, 2006.
7. M. Houtsma, and A. Swami, "Set-oriented mining of association rules", Research Report RJ 9567, IBM Almaden Reseach Center, 1993.
8. J. Mata, J. L. Alvarez, and J. C. Riquelme, "An evolutionary algorithm to discover numeric association rules", In Proceedings of the ACM symposium on Applied computing SAC, 2002, pp. 590-594.
9. M. Nasiri, L. Taghavi, and B. Minaei, "Multi-Objective Rule Mining using Simulated Annealing Algorithm", Soft Computing, JCDA journal Korea, Vol. 5, No. 1, Feb. 2010, pp. 60-68.
10. SPSSInc. "Clementine 12.0 Algorithms Guide". http://www.spss.com.
11. X. Yan, CH. Zhang, and SH. Zhang, "Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support", Elsevier Expert Systems with Applications, Vol. 36, No. 2, 2008, pp. 3066-3076.