Journal of Digital Information Management


Vol No. 19 ,Issue No. 4 2021

The Imperialist Competitive Algorithm for Automated Mining of Association Rules
Fariba Khademolghorani
Department of Computer Engineering Islamic Azad University Najafabad Branch, Iran
Abstract: Association rule mining is an optimization problem because of several limitations. Recently, the imperialist competitive algorithm (ICA) has been submitted for solving different optimization problems. This algorithm is based on the socio-political competition among empires. This paper proposes a novel ICA algorithm for automated mining of the exciting and readable association rules without considering the minimum support and confidence thresholds. The convergence rate and computational efficiency of ICA have been improved. This study shows that ICA is combined with some operators of genetic algorithms. The experimental results show that this algorithm is more efficient than the methods of mining association rules based on the basic ICA and the genetic algorithm. These modifications are not only valid for association rule mining but also have extensions to other optimization problems.
Keywords: Association Rules, Imperialist Competitive Algorithm, Genetic Algorithm, Evolutionary Algorithm The Imperialist Competitive Algorithm for Automated Mining of Association Rules
DOI:https://doi.org/10.6025/jdim/2021/19/4/135-143
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References:

[1] Han, J., Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann in Elsevier, Second Edition, San Francisco.
[2] Agrawal, R., Imielinksi, T., Swami, A. (1993). Database mining: a performance perspective, In: Proceedings of IEEE Transactions on Knowledge and Data, 5 (6) 914–925.
[3] Saggar, M., Agrawal, A.K., Lad, A. (2004). Optimization of association rule mining using improved genetic algorithms, In.: Proceedings of the IEEE International Conference on Systems Man and Cybernetics, p. 3725– 3729.
[4] Agrawal, R., Imielinski, T., Swami, A.N. (1993). Mining association rules between sets of items in large databases, In.: Proceedings of ACM SIGMOD international conference on Management of data, Washington, 22 (2) 207–216.
[5] Houtsma, A., Swami, M. (1993). Set-oriented mining of association rules, Research Report RJ 9567, IBM Almaden Research Center, San Jose, California.
[6] Birn, S., Motvani, R., D.Ullman, J., Tusr, S. (1997). Dynamic itemset counting and implication rules for market basket data, In: Proceedings of the ACM SIGMOD international conference on Management of data, New York, 26 (2) 255–264.
[7] Khademolghorani, F. (2011). An effective algorithm for mining association rules based on Imperialist Competitive Algorithm, In: Proceedings of the sixth International Conference on Digital Information Management (ICDIM 2011), Melbourne, Austria, p. 6-11.
[8] Kuo, R.J., Chao, C.M., Chiu, Y.T. (2009). Application of particle swarm optimization to association rule mining, Elsevier Applied Soft Computing, 11(1) 326-336.
[9] Agrawal, R., Srikant, R. (1994). Fast algorithm for mining association rules in large databases, Research Report RJ 9839, IBM Almaden Research Center, San Francisco, CA, USA, p. 487-499.
[10] Srikant, R., Agrawal, R. (1996). Mining quantitative association rules in large relational tables, In.: Proc. of ACM SIGMOD international conference on Management of data, Montreal, 25 (2) 1-12.
[11] Mata, J., Alvarez, J., Riquelme, J. (2002). Discovering numeric association rules via evolutionary algorithm, In.: Proc of sixth Pacific–Asia conference on knowledge discovery and data mining PAKDD-02 (LNAI), Taiwan. p. 40-51.
[12] Li, C., Yang, M. (2004). Association rule data mining in manufacturing information system based on genetic algorithms, In: Proceedings of the 3rd International Conference on Computational Electromagnetics and Its Applications, p. 153–156.
[13] Kuo, R.J., Shih, C.W. (2007). Association rule mining through the ant colony system for National Health Insurance Research Database in Taiwan, Computers an Mathematics with Applications, Tarrytown, NY, USA, 54 (11-12) 1303–1318.
[14] Kuo, R.J., Lin, S.Y., Shih, C.W. (2007). Discovering association rules through ant colony system for medical database in Taiwan, International Journal of Expert Systems with Applications, 33 (3) 794-808.
[15] Ghosh, A., Nath, B. (2004). Multi-Objective Associa tion Rule Mining Using Genetic Algorithm, Elsevier Information Sciences, 163 (1) 123–133.
[16] Badawy, O.M., Habib, M.I., Sallam, A.A. (2008). Quantitative Association Rule Mining Using a Hybrid PSO/ ACO Algorithm (PSO/ACO-AR), In: Proceedings of Arab Conference on Information Technology (ACIT’2008), Hammamet, Tunisia, p. 1-9.
[17] Yan, X., Zhang, Ch., Zhang, Sh. (2009). Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support, processing of Expert Systems with Applications, 36 (2) 3066-3076 .
[18] Qodmanan, H., Nasiri, M., Minaei-Bidgoli, B. (2010). Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence, In.: Proceedings of Elsevier Expert Systems with Applications, 38 (1) 288-298.
[19] Nasiri, M., Taghavi, L., Minaei-Bidgoli, B. (2010). Multi-Objective Rule Mining using Simulated Annealing Algorithm, Soft Computing in Journal of Convergence Information Technology (JCDA), Korea, 5 (1) 60-68.
[20] Khademolghorani, F., Baraani, A., Zamanifar, K. (2011). Efficient mining of association rules based on gravitational search algorithm, IJCSI International Journal of Computer Science Issues, Mahebourg, 8 (4) 1-8.
[21] Atashpaz-Gargari, E., Lucas, C. (2007). Imperialist Competitive Algorithm An Algorithm for Optimization Inspired by Imperialistic Competition, IEEE Cogress on Evolutionary Compution, Singapore, p. 4661-4667.
[22] Rose Tinabo. (2011). A Mechanism for Selecting Appropriate Data Mining Techniques, Journal of Intelligent Computing, 2 (1) 35-41. March.
[23] Quanyin Zhu., Pei Zhou., Suqun Cao., Yunyang Yan., Jin Ding. (2012). A Novel RDB-SW Approach for Commodities Price Dynamic Trend Analysis Based on Web Mining, Journal of Digital Information Management, 10 (4) 168-173. August.
[24] Yang Hang., Simon Fong. (2011). Algorithmic level stream mining for Business Intelligence System Architecture building, International Journal of Web Applications, 3 (1) 29-35. March, 2011.
[25] Mohamed El Ghourabi., Amira Dridi., Fedya Telmoudi. (2011). Data Mining versus Statistical Tools for Value at Risk Estimation, Journal of Information Technology Review, 2 (4) 154-162. November, 2011. [26] Hassan I. Abdalla. (2011). New Technique to Deal with Dynamic Data Mining in the Database, Journal of Digital Information Management, 9 (4) 147-152. August 2011.