Mining Fuzzy Association Rules on Has-A and Is-A Hierarchical Structures
Been-Chian Chien*, Ming-Huang Zhong**, and Jeng-Jung Wang**
*Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Su-Lin Street, Tainan 70005, Taiwan, R.O.C.
**Department of Information Engineering, I-Shou University Kaohsiung, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, R.O.C.
Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The proposed method first reduces a Has-A & Is-A hierarchy into an extended Has-A hierarchy using the IsA-Reduce algorithm. The quantitative data is transformed into fuzzy items. The RPFApriori algorithm is then applied to find fuzzy association rules from the fuzzy item data and the extended Has-A hierarchy.
-  R. Agrawal, T. Imilienski, 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, pp. 207-216, 1993.
-  R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules in Large Databases,” in Proc. of the 20th Int’l Conf. on VLDB, pp. 487-499, 1994.
-  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’l Conf. on Management of Data, 1997.
-  M. S. Chen, J. S. Park, and P. S. Yu, “Efficient Data Mining for Path Traversal Patterns,” in Proc. of the IEEE Transactions on Knowledge and Data Engineering, Vol.10, No.2, pp. 209-220, 1998.
-  G. Chen and Q.Wei, “Fuzzy association rules and the extended mining algorithms,” Information Sciences, Vol.147, No.1-4, pp. 201-228, 2002.
-  T. Fukuda, Y. Morimoto, S. Morishita, and T, Tokuyama. “Mining optimized association rules from numeric attributes,” in Proc. of the ACMSIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 1996.
-  J. Han and Y. Fu, “Discovery of multiple-level association rules from large database,” in proc. of the Int’l Conf. on VLDB, 1995.
-  J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. of the ACM SIGMOD Int’l Conf. Management of Data, 2000.
-  T. P. Hong, C. S. Kuo, and S. C. Chi, “Mining association rules from quantitative data,” Intelligent Data Analysis, Vol.3, No.5, pp. 363-376, 1999.
-  T. P. Hong, K. Y. Lin, and S. L. Wang, “Fuzzy data mining for interesting generalized association rules,” Fuzzy Sets and Systems, Vol.138, No.2, pp. 255-269, 2003.
-  Y. F. Huang and C. M. Wu, “Mining generalized association rules using pruning techniques,” in Proc. of the IEEE Int’l Conf. on Data Mining, 2002.
-  K. F. Jea, T. P. Chiu, and M. Y. Chang, “Mining Multiple-Level Association Rules in Has-A Hierarchy,” in Proc. of the Conf. on AI, Fuzzy System, and Grey System, 2003.
-  K. M. Lee, “Mining Generalized Fuzzy Quantitative Association Rules with Fuzzy Generalization Hierarchies,” IFSA World Congress and 20th NAFIPS International Conference, 2001.
-  J. Lu, B. Xu, and J. Jiang, “A prediction method of fuzzy association rules,” in Proc. of the IEEE Int’l Conf. on Information Reuse and Integration, 2003.
-  C. L. Lui and F. L. Chung, “Discovery of generalized association rules with multiple minimum supports,” in Proc. of the 4th European Conf. on Principles and Practice of Knowledge Discovery in Databases, pp. 510-515, 2000.
-  H. Mannila, H. Toivonen, and A. I. Verkamo, “Efficient Algorithms for discovering association rules,” AAAI Workshop on Knowledge Discovery in Databases, 1994.
-  J. S. Park, M. S. Chen, and P. S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” in Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, 1995.
-  R. Rastogi and K. Shim, “Mining optimized association rules with categorical and numeric attributes,” in Proc. of the 14th Int’l Conf. on Data Engineering, 1998.
-  A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” in Proc. of the 21st Int’l Conf. on VLDB, pp. 432-444, 1995.
-  F. Y. Shih, C. M. Yang, and C. C. Wang, “An Effective Boolean Algorithm for Discovering the Multiple-level Frequent Itemsets,” Leader University, Taiwan, 2003.
-  R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” in Proc. of the Int’l Conf. on VLDB, 1995.
-  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, pp. 1-12, 1996.
-  K. Sriphaew and T. Theeramunkong, “A new method for finding generalized frequent itemsets in generalized association rule mining,” in Proc. of the 7th Int’l Symposium on Computers and Communications, 2002.
-  H. Toivonen, “Sampling large databases for association rules,” in Proc. of the 22nd Int’l Conf. on VLDB, pp. 134-145, Sept., 1996.
-  T. Watanabe and M. Nakayama, “Fuzzy rule extraction based on the mining generalized association rules,” in Proc. of the Int’l Conf. on Systems, Man and Cybernetics, 2003.
-  I. Weber, “On Pruning Strategies for Discovery of Generalized and Quantitative Association Rules,” in Proc. of the Knowledge Discovery and Data Mining Workshop, 1998.
-  Q. Wei and G. Chen, “Mining generalized association rules with fuzzy taxonomic structures,” in Proc. of the 18th Int’l Conf. on NAFIPS, 1999.
-  L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol.8, No.3, pp. 338-353, 1965.
-  W. Zhang, “Mining Fuzzy Quantitative Association Rules,” in Proc. of the 11th IEEE Int’l Conf. on Tools with Artificial Intelligence, 1999.
-  M. H. Zhong, B. C. Chien, and J. J. Wang, “Mining Fuzzy Composite Association rules on Has-A Hierarchy from Databases,” in Proc. of the 9th conf. on artificial intelligence and applications, 2004.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.