Abstract
Many effective algorithms have been developed to mine association rules in relational and transactional data separately. In this paper, we present a technique for the mining of such rules in databases containing both types of data. This technique, which we call Fuzzy Miner, performs its tasks by the use of fuzzy logic, a set of transformation functions, and by residual analysis. With the transformation functions, new attributes and new item types can be derived for either relational or transactional data. They also make it possible for association rules relating the two types of data to be discovered, e.g., the buying patterns related to the demographics of a group of customers. With fuzzy logic, Fuzzy Miner is not only able to discover Boolean and quantitative but also fuzzy association rules. This makes the patterns discovered more easily understandable by human users and more resilient to noise and missing data values. With residual analysis, Fuzzy Minder does not require any user-supplied thresholds that are often hard to determine. The Fuzzy Miner also discovers relationship between fuzzy and quantitative values and allows quantitative values to be inferred by the rules. With these features, Fuzzy Miner can be applied to real-life databases containing relational and transactional data.
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References
R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” in Proc. of the ACM SIGMOD Int’l Conf on Management of Data, Washington D. C., May 1993, pp. 207–216.
W.-H. Au and K.C.C. Chan, “An Effective Algorithm for Discovering Fuzzy Rules in Relational Databases,” in Proc. of the 1998 IEEE Int’l Conf. on Fuzzy Systems, Anchorage, Alaska, May 1998, pp. 1314–1319.
W.-H. Au, and K.C.C. Chan, “FARM: A Data Mining System for Discovering Fuzzy Association Rules,” in Proc. of the 1999 IEEE Intl Conf. on Fuzzy Systems, Seoul, Korea, Aug. 1999.
K.C.C. Chan, and W.-H. Au, “Mining Fuzzy Association Rules,” in Proc. of the 6th ACM Int’l Conf. on Information and Knowledge Management, Las Vegas, Nevada, Nov. 1997, pp. 209–215.
K.C.C. Chan, and A.K.C. Wong, “APACS: A System for the Automatic Analysis and Classification of Conceptual Patterns,” Computational Intelligence, vol. 6, pp. 119–131, 1990.
M.-S. Chen, J. Han, and P.S. Yu, “Data Mining: An Overview from A Database Perspective,” IEEE Trans. on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866–883, Dec. 1996.
V. Dhar and A. Tuzhilin, “Abstract-Driven Pattern Discovery in Databases,” IEEE Trans. Knowledge and Data Engineering, vol. 5, no. 6, pp. 926–938, 1993.
U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAIIMIT Press, 1996.
J. Han and Y. Fu, “Discovery of Multiple-Level Association Rules from Large Databases,” in Proc. of the 21st VLDB Conf, Zurich, Switzerland, 1995, pp. 420–431.
D.H. Lee and M.H. Kim, “Database Summarization Using Fuzzy ISA Hierarchies,” IEEE Trans. on Systems, Man, and Cybernetics — Part B: Cybernetics, vol. 27, no. 4, pp. 671–680, Aug. 1997.
W. Pedrycz, “Data Mining and Fuzzy Modeling,” in Proc. of 1996 Biennial Conf of the North American Fuzzy Information Processing Society, Berkeley, California, June 1996, pp. 263–267.
G. Piatetsky-Shapiro and W.J. Frawley (Eds.), Knowledge Discovery in Databases, AAAI/MIT Press, 1991.
R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” in Proc. of the 21st VLDB Conf., Zurich, Switzerland, 1995, pp. 407–419.
R. Srikant and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables,” in Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, Monreal, Canada, June 1996, pp. 1–12.
R.R. Yager, “On Linguistic Summaries of Data,” in [12], pp. 347–363.
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Chan, K.C.C., Au, WH. (2001). Mining Fuzzy Association Rules in a Database Containing Relational and Transactional Data. In: Kandel, A., Last, M., Bunke, H. (eds) Data Mining and Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 68. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1825-3_4
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DOI: https://doi.org/10.1007/978-3-7908-1825-3_4
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