Journal of Applied Information Science

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Multilevel Interesting Association Rule Mining Using Soft Computing Techniques


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1 Department of Information Technology, A. D. Patel Institute of Technology (ADIT), Gujarat, India
     

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Data warehouse contains large amounts of data from a various sources that may contain some noise while using for decision making. Data mining is extraction of knowledge from large data which may contains some amount of missing data along with inaccurate data and outliers. One of the best ways to detect data errors is by properly utilizing association rules that indicates relationships among attributes. Association rule mining algorithms detects patterns which occur in large dataset. Mining association rules at multiple level of concept hierarchy lead to the detection of more specific and actual knowledge from the dataset. The present paper uses various soft computing approaches for mining multilevel interesting association rules. In real-world problems, transaction data contains quantitative values. The fuzzy logic is useful for finding interesting association rules in quantitative transactions. To generate optimized multilevel association rule, optimization techniques such as genetic algorithm, ant colony optimization and particle swarm optimization are used. In this paper, soft computing techniques are reviewed based on approach used, findings and open issues in order to find optimized multilevel interesting association rules.

Keywords

Ant Colony System, Fuzzy Logic, Genetic Algorithm, Interestingness Measures, Multilevel Association Rule Mining, Particle Swarm Optimization.
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  • R. Alcala, J. Alcala-Fdez, M. J. Gacto, and F. Herrera, “Genetic learning of membership functions for mining fuzzy association rules,” in IEEE Int. Conf. on Fuzzy Systems, pp. 1-6, 2007.

  • A. M. N. Kousari, S. J. Mirabedini, and E. Ghasemkhani, “Improvement of mining fuzzy multiple level association rules from quantitative data,” Journal of Software Engineering and Applications, vol. 5, no. 3, pp. 190-199, 2012.

  • A. Kandel, Fuzzy Expert Systems, CRC Press, Boca Raton, pp. 8-19, 1992.

  • D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.

  • S. N. Sivanandam, and S. N. Deepa, Introduction to Genetic Algorithms, Springer-Verlag, Berlin, Heidelberg, 2008.

  • K. K. Bharadwaj, N. M. Hewahi, and M. A. Brando, “Adaptive hierarchical censored production rule-based system: A genetic algorithm approach, advances in artificial intelligence,” SBIA ‘96, Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin, Germany, vol. 1159, pp. 81-90, 1996.

  • J. Kennedy, and R. C. Eberhart, “Particle swarm optimization,” in Proc. of the Conf. on Neural Networks, IEEE, pp. 1942-1948, 1995.

  • R. C. Eberhart, and J. Kennedy, “A new optimizer using particles swarm theory,” in IEEE Proc. of the 6th Int. Symp. on Micro Machine and Human Science, pp. 39-43, 1995.

  • M. Dorigo, and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, pp. 53-66, 1997.

  • E. V. Mahmoudi, V. Aghighi, M. N. Torshiz, M. Jalali, and M. Yaghoobi, “Mining generalized fuzzy association rules via determining minimum supports,” The 19th Iranian Conf. on Electrical Engineering (ICEE), pp. 1-6, May 2011.

  • J. Han, and Y. Fu, “Mining multiple-level association rules in large databases,” IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 5, pp. 798-805, 1999.

  • P. Arora, R. K. Chauhan, and A. Kush, “Frequent itemsets from multiple datasets with fuzzy data,” International Journal of Computer Theory and Engineering, vol. 3, no. 2, pp. 255-260, 2011.

  • Y. Wan, Y. Liang, and L. Ding, “Mining multilevel association rules with dynamic concept hierarchy,” IEEE Int. Conf. on Machine Learning and Cybernetics,” pp. 287-292, 2008.

  • J. Han, and M. Kamber, Data Mining Concepts & Techniques, Morgan Kaufmann Publishers, San Francisco, 2004.

  • T. P. Hong, K. Y. Lin, and S. L. Wang, “Fuzzy data mining for interesting generalized association rules,” Fuzzy Sets and Systems, Elsevier, vol. 138, no. 2, pp. 255-269, 2003.

  • M. Kaya, and R. Alhajj, “Mining multi-cross-level fuzzy weighted association rules,” 2nd IEEE Int. Conf. on Intelligent System, pp. 225-230, 2004.

  • W. Y. Lin, and M. C. Tseng, “Automated support specification for efficient mining of interesting association rules,” Journal of Information Science, vol. 32, pp. 238-250, 2006.

  • S. Lallich, O. Teytaud, and E. Prudhomme, “Association rule interestingness: Measure and statistical validation,” Quality Measures in Data Mining, Studies in Computational Intelligence (SCI), Springer, vol. 43, pp. 251-275, 2007.

  • P. W. Peter, and B. Venansius, “Extraction of interesting association rules using genetic algorithms,” International Journal of Computing and ICT Research, vol. 2, no. 1, pp. 26-33, 2008.

  • Y. B. Wan, Y. Liang, and L. Y. Ding, “Mining multilevel association rules with dynamic concept hierarchy,” IEEE Proc. of the 7th Int. Conf. on Machine Learning and Cybernetics, Kunming, pp. 287-292, 2008.

  • T. Aydın, and H. A. Guvenir, “Modeling interestingness of streaming association rules as a benefit-maximizing classification problem,” Knowledge-Based Systems, Elsevier, vol. 22, no. 1, pp. 85-99, 2009.

  • Y. Xu, G. Shaw, and Y. Li, “Concise representations for association rules in multi-level datasets,” Journal of Systems Science and Systems Engineering, Springer, vol. 18, no. 1, pp. 53-70, 2009.

  • T. P. Hong, Y. F. Tung, S. L. Wang, and Y. L. Wu, “A multilevel ant based algorithm for fuzzy data mining,” The 28th North American Fuzzy Information Proc. Society Annual Conf., Cincinnati, Ohio, USA, pp. 1-5, 2009.

  • G. Shaw, Y. Xu, and S. Geva, “Eliminating redundant association rules in multilevel datasets,” 4th Int. Conf. on Data Mining, Las Vegas, Nevada, USA, pp. 14-17, 2009.

  • P. Gautam, and K. R. Pardasani, “A fast algorithm for mining multilevel association rule based on Boolean matrix,” International Journal on Computer Science and Engineering (IJCSE), vol. 2, no. 3, pp. 746-752, 2010.

  • T. P. Hong, Y. F. Tung, S. L. Wang, Y. L. Wu, and M. T. Wu, “A multi-level ant-colony mining algorithm for membership functions,” Information Sciences, Elsevier, vol. 182, no. 1, pp. 3-14, 2012.

  • S. Prakash, M. Vijayakumar, and R. M. S. Parvathi, “A novel method of mining association rule with multilevel concept hierarchy,” International Journal of Computer Applications (IJCA), pp. 26-29, 2011.

  • P. Gautam, and K. R. Pardasani, “Efficient method for multiple-level association rules in large databases,” Journal of Emerging Trends in Computing and Information Sciences, vol. 2, no. 12, pp. 722-732, 2011.

  • R. J. Kuo, C. M. Chao, and Y. T. Chiu, “Application of particle swarm optimization to association rule mining,” Applied Soft Computing, Elsevier, vol. 11, pp. 326-336, 2011.

  • S. J. Mirabedini, A. M. N. Kousari, and M. Sadeghzad, “Improvement of mining fuzzy multiple level association rules from quantitative data,” World Applied Sciences Journal, pp. 1556-1566, 2012.

  • B. Rani, and S. Aggarwal, “Optimization of association rule mining techniques using ant colony optimization,” International Journal of Current Engineering and Technology, vol. 3, no. 5, pp. 1804-1808, 2013.

  • M. K. Gupta, and G. Sikka, “Association rules extraction using multi-objective feature of genetic algorithm,” Proc. of the World Congr. on Engineering and Computer Science, San Francisco, USA, vol. 2, pp. 23-25, 2013.

  • S. Tyagi, and K. K. Bharadwaj, “Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining,” Swarm and Evolutionary Computation, Elsevier, vol. 13, pp. 1-12, 2013.

  • K. N. V. D. Sarath, and V. Ravi, “Association rule mining using binary particle swarm optimization,” Engineering Applications of Artificial Intelligence, Elsevier, vol. 26, no. 8, pp. 1832-1840, 2013.

  • A. K. Chandanan, and M. K. Shukla, “Removal of duplicate rules for association rule mining from multilevel dataset,” Procedia Computer Science, Elsevier, vol. 45, pp. 143-149, 2015.

  • M. Narvekar, and S. F. Syed, “An optimized algorithm for association rule mining using FP tree,” Procedia Computer Science, Elsevier, vol. 45, pp. 101-110, 2015.

  • R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” Proc. Int. Conf. of ACM-SIGMOD on Management of Data, pp. 207-216, 1993.

  • E. R. Omiecinski, “Alternative interest measures for mining associations in databases,” IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 1, pp. 57-69, 2003.

  • P. N. Tan, V. Kumar, and J. Srivastava, “Selecting the right objective measure for association analysis,” Information Systems, vol. 29, no. 4, pp. 293-313, 2004.

  • A. Ghosh, and B. Nath, “Muti-objective rule mining using genetic algorithms,” Information Sciences, vol. 163, no. 1-3, pp. 123-133, 2004.

  • T. Brijs, K. Vanhoof, and G. Wets, “Defining interestingness for association rules,” International Journal of Information Theories & Applications, vol. 10, no. 4, pp. 370-375, 2003.

  • S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, “Dynamic itemset counting and implication rules for market basket data,” in Proc. of the ACM SIGMOD, Int. Conf. on Management of Data (ACM SIGMOD’97), USA, pp. 255-264, 1997.

  • M. A. Hahsler, “Probabilistic comparison of commonly used interest measures for association rules,” 2015. [Online]. Available: http://michael.hahsler.net/research/association_rules/measures.html


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  • Multilevel Interesting Association Rule Mining Using Soft Computing Techniques

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Authors

Dinesh J. Prajapati
Department of Information Technology, A. D. Patel Institute of Technology (ADIT), Gujarat, India

Abstract


Data warehouse contains large amounts of data from a various sources that may contain some noise while using for decision making. Data mining is extraction of knowledge from large data which may contains some amount of missing data along with inaccurate data and outliers. One of the best ways to detect data errors is by properly utilizing association rules that indicates relationships among attributes. Association rule mining algorithms detects patterns which occur in large dataset. Mining association rules at multiple level of concept hierarchy lead to the detection of more specific and actual knowledge from the dataset. The present paper uses various soft computing approaches for mining multilevel interesting association rules. In real-world problems, transaction data contains quantitative values. The fuzzy logic is useful for finding interesting association rules in quantitative transactions. To generate optimized multilevel association rule, optimization techniques such as genetic algorithm, ant colony optimization and particle swarm optimization are used. In this paper, soft computing techniques are reviewed based on approach used, findings and open issues in order to find optimized multilevel interesting association rules.

Keywords


Ant Colony System, Fuzzy Logic, Genetic Algorithm, Interestingness Measures, Multilevel Association Rule Mining, Particle Swarm Optimization.

References