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Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules
Abstract:In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076742Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1470
 Agrawal, R. and Srikant, R., "Fast algorithms for mining association rules," in Proceedings of 1994 International Conference on Very Large Data Bases, pp.487-499, 1994.
 Berry, M. and Linoff, G., Data Mining Techniques: for marketing, sales, and customer support, John Wiley & Sons, NY, 1997.
 Chen, S. M., Jong, W. T., "Fuzzy query translation for relational database systems," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 27, no. 4, pp. 714-721, 1997.
 Han, J. W. and Kamber, M., Data Mining : Concepts and Techniques, Morgan Kaufmann, San Francisco, 2001.
 Han, J., Pei, J., and Yin, Y., "Mining Frequent Patterns without Candidate Generation," in Proc. ACM SIGMOD Int. Conf. on Management of Data, pp, 1-12, 2000.
 Hong, T. P., & Chen, J. B., "Find relevant attributes and membership functions," Fuzzy Sets and Systems, Vol. 103, no. 3, pp.389-404, 1999.
 Hu Y. C,. "Mining association rules at a concept hierarchy using fuzzy partition," Journal of Information Management, Vol. 13, no.3, pp.63-80, 2006.
 Hu, Y. C., Chen, R. S. and Tzeng, G. H., "Finding Fuzzy Classification Rules Using Data Mining Techniques," Pattern Recognition Letters, vol. 24, pp.509-519, 2003.
 Ishibuchi, H., Nakashima, T., and Murata, T. (1999), "Performance Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern Classification Problems," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29, no.5, pp.601-618, 1999.
 Jang, J. S. R., Sun, C. T. and Mizutani, E., Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentic-Hall, NJ, 1997.
 Myra, S., "Web usage mining for web site evaluation," Communications of the ACM, Vol. 43, pp. 21-30, 1994.
 Pedrycz, W., "Why triangular membership functions?," Fuzzy Sets and Systems, Vol. 64, pp. 21-30, 1994.
 Tan. P. N., Michael Mteinbach, Vipin Kumar, Introduction to Data Mining, NY: Addison Wesley, 2005
 Wang, L. X. and Mendel, J. M. (1992), "Generating Fuzzy Rules by Learning from Examples", IEEE Transactions on Systems, Man, and Cybernetisc, Vol. 22, no.6, pp.1414-1427, 1992.
 Zadeh, L. A. (1965), "Fuzzy Sets," Information Control, vol. 8, no.3, pp.338-353, 1965.
 Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning," Information Science (part 1), Vol. 8, no. 3, pp.199-249, 1975.
 Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning," Information Science (part 2), Vol. 8, no. 4, pp.301-357, 1975.
 Zadeh, L. A. (1976), "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning," Information Science (part 3), Vol. 9, no. 1, pp.43-80, 1976.
 Zimmermann, H. -J., Fuzzy sets, Decision making and expert systems, Kluwer, Boston, 1991.