Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
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Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.
- Arslan, A., & Kaya, M. (2001). Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets and Systems, 118(2), 297–306. CrossRef
- Au, W. H., & Chan, K. C. C. (1998). An effective algorithm for discovering fuzzy rules in relational databases. Proceedings of IEEE International Conference on Fuzzy Systems, 1314–1319.
- Chan, K. C. C., & Au, W. H. (1997). Mining fuzzy association rules. Proceedings of ACM International Conference on Information and Knowledge Management, Las Vegas, pp. 209–215.
- Chien, B. C., Lin, Z. L., & Hong, T. P. (2001). An efficient clustering algorithm for mining fuzzy quantitative association rules. Proceedings of IFSA World Congress and NAFIPS International Conference, Vol. 3, pp. 1306–1311.
- Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In S. Forrest (Ed.), Proceedings of the International Conference on Genetic Algorithms (pp. 416–423). San Mateo, CA.
- Fu, A. W. C., Wong, M. H., Sze, S. C., Wong, W. C., Wong, W. L., Yu, W. K. (1998). Finding fuzzy sets for the mining of association rules for numerical attributes. Proceedings of the International Symposium of Intelligent Data Engineering and Learning, pp. 263–268.
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
- Guha, S., Rastogi, R., & Shim, K. (2001). Cure: An efficient clustering algorithm for large databases. Information Systems, 26(1), 35–58. CrossRef
- Gyenesei, A. (2000). A fuzzy approach for mining quantitative association rules. TUCS Technical Report No.336.
- Herrera, F., Lozano, M., & Verdegay, J. L. (1998). Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review, 12(4), 265–319, August. CrossRef
- Hirota, K., & Pedrycz, W. (1996). Linguistic data mining and fuzzy modelling. Proceedings of IEEE International Conference on Fuzzy Systems, 2, 1488–1496.
- Holland, J. H. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press, The MIT Press edition.
- Hong, T. P., Chen, C. H., Wu, Y. L., & Lee, Y. C. (2004). Using divide-and-conquer GA strategy in fuzzy data mining. Proceedings of the IEEE Symposium on Computers and Communications.
- Hong, T. P., Kuo, C. S., & Chi, S. C. (1999a). A fuzzy data mining algorithm for quantitative values. Proceedings of the International Conference on Knowledge-Based Intelligent Information Engineering Systems, pp. 480–483.
- Hong, T. P., Kuo, C. S., & Chi, S. C. (1999b). Mining association rules from quantitative data. Intelligent Data Analysis, 3, 363–376. CrossRef
- Ishibuchi, H., Nakashima, T., & Yamamoto, T. (2001). Fuzzy association rules for handling continuous attributes. Proceedings of IEEE International Symposium on Industrial Electronics, pp. 118–121.
- Kaya, M., Alhajj, R., Polat, F., & Arslan, A. (2002). Efficient automated mining of fuzzy association rules. Proceedings of the International Conference on Database and Expert Systems with Applications.
- Kuok, C. M., Fu, A. W., & Wong, M. H. (1998). Mining fuzzy association rules in databases. SIGMOD Record, 17(1), 41–46. CrossRef
- Lent, B., Swami, A., & Widom, J. (1997). Clustering association rules. Proceedings of IEEE International Conference on Data Engineering, pp. 220–231.
- Michalewicz, Z. (1992). Genetic algorithms + data structures = evolution programs. Berlin: Springer.
- Miller, R. J., & Yang, Y. (1997). Association rules over interval data. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 452–461.
- Ng, R., & Han, J. (1994). Efficient and effective clustering methods for spatial data mining. Proceedings of the International Conference on Very Large Databases.
- Pedrycz, W. (1998). Fuzzy sets technology in knowledge discovery. Fuzzy Sets and Systems, 98, 279–290. CrossRef
- Srikant, R., & Agrawal, R. (1996). Mining quantitative association rules in large relational tables. Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 1–12.
- Veldhuizen, D. A. V., & Lamont, G. B. (1998). Multi-objective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03. Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Ohio.
- Wang, W., & Bridges, S. M. (2000). Genetic algorithm optimization of membership functions for mining fuzzy association rules. Proceedings of the International Conference on Fuzzy Theory & Technology, pp. 131–134.
- Yager, R. R. (1995). Fuzzy summaries in database mining. Proceedings of the Conference on Artificial Intelligence for Application, pp. 265–269.
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. CrossRef
- Zhang, W. (1999). Mining fuzzy quantitative association rules. Proceedings of IEEE International Conference on Tools with Artificial Intelligence (pp. 99–102). Illinois.
- Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271. CrossRef
- Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Journal of Intelligent Information Systems
Volume 31, Issue 3 , pp 243-264
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- Automated clustering
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- Fuzzy association rules
- Multi-objective genetic algorithms
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