JACIII Vol.10 No.3 pp. 287-294
doi: 10.20965/jaciii.2006.p0287


Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases

Jianchao Han, and Mohsen Beheshti

Department of Computer Science, California State University, Dominguez Hills, 1000 E. Victoria St., Carson, CA 90747, USA

February 22, 2005
December 21, 2005
May 20, 2006
data mining and knowledge discovery, association rules, positive and negative association rules, fuzzy association rules
Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.
Cite this article as:
J. Han and M. Beheshti, “Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.3, pp. 287-294, 2006.
Data files:
  1. [1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proc. of ACM SIGMOD Conf. on Management of Data, pp. 207-216, 1993.
  2. [2] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. Verkamo, “Fast Discovery of Association Rules, Advances in Knowledge Discovery and Data Mining,” ed. by U. Fayyard, G. Piatetsky-Shapiro, P. Smyth, R. Uthrusamy, AAAI Press, pp. 307-328, 1996.
  3. [3] M. Antonie, and O. Zaiane, “An Associative Classifier Based on Positive and Negative Rules,” Proc. of 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 64-69, 2004.
  4. [4] P. Bosc, D. Dubois, O. Pivert, and H. Prade, “On Fuzzy Association Rules Based on Fuzzy Cardinalities,” Proc. of IEEE Internal. Fuzzy Systems Conf., pp. 461-464, 2001.
  5. [5] P. Bosc, and O. Pivert, “On some Fuzzy Extensions of Association Rules,” Proc. of Joint 9th IFSA World Congress and 20th NAFIPS Internal. Conf., pp. 1104-1109, 2001.
  6. [6] S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Baskets: Generalizing Association Rules to Correlations,” Proc. of ACM SIGMOD internal. Conf. on Management of Data, pp. 265-276, 1997.
  7. [7] G. Chen, Q. Wei, and E. E. Kerre, “Fuzzy Data Mining: Discovery of Fuzzy Generalized Association Rules,” Recent Research Issues on Management of Fuzziness in Databases, ed. By G. Bordogna, G. Pasi, Springer Physica-Verlag, pp. 45-66, 2000.
  8. [8] B. Chien, Z. Lin, and T. Hong, “An Efficient Clustering Algorithm for Mining Fuzzy Quantitative Association Rules,” Proc. of 9th Internal. Fuzzy Systems Association World Congress, pp. 1306-1311, 2001.
  9. [9] M. De Cock, C. Cornelis, and E. E. Kerre, “Fuzzy Association Rules: a Two-Sided Approach,” Proc. of Internal. Conf. on Fuzzy Information Processing-Theories and Applications, pp. 385-390, 2003.
  10. [10] D. Dubois, E. Hullermeier, and H. Prade, “A Note on Quality Measures for Fuzzy Association Rules,” Lecture Notes in Artificial Intelligemce, 2715, pp. 346-353, 2003.
  11. [11] A. Fu, “Finding fuzzy sets for the mining of fuzzy association rules for umerical attributes,” Proc. of 1st Int’l Symposium on Intelligent Data Enginerring and Learning, pp. 263-268, 1998.
  12. [12] A. Gyenesei, “Mining Weighted Association Rules for Fuzzy Quantitative Items,” Proc. of PKDD Conference, pp. 416-423, 2000.
  13. [13] J. Han, and Y. Fu, “Discovery of multiple-level association rules,” Proc. of 21st Int’l Conf. on Very Large Data Bases, pp. 420-432, 1995.
  14. [14] E. Hullermeier, “Fuzzy Association Rules: Semantic Issues and Quality Measures,” Lecture Notes in Computer Science 2206, Springer, pp. 380-391, 2001.
  15. [15] E. Hullermeier, “Mining Implication-Based Fuzzy Association Rules in Databases,” Proc. of 9th Int’l Conf. on Info. Processing and Management of Uncertainty in Knowledge-Based Systems I: pp. 101-108, 2002.
  16. [16] A. Savasere, E. Omiecinski, and S. Navathe, “Mining for Strong Negative Associations in a Large Databases of Customer Transactions,” Proc. of Int’l Conf. on Data Engineering, pp. 491-502, 1998.
  17. [17] R. Srikant, and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables,” Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, pp. 1-12, 1996.
  18. [18] R. Srikant, and A. Agrawal, “Mining Generalized Association Rules,” Proc. of 21th Int’l Conf. on Very Large Databases, pp. 407-419, 1995.
  19. [19] S. Srikant, Q. Vu, and R. Agrawal, “Mining Association Rules with Item Constraints,” Proc. of ACMKDD Internal. Conf. on Knowledge Discovery and Data Mining, pp. 67-73, 1997.
  20. [20] X. Wu, C. Zhang, and S. Zhang, “Mining both Possitive and Negative Association Rules,” Proc. of 19th Internal. Conf. on Data Machine Learning, pp. 658-665, 2002.
  21. [21] L. A. Zadeh, “Fuzzy Sets,” Information and Control, 8, pp. 338-353, 1965.
  22. [22] C. Zhang, and S. Zhang, “Association Rule Mining – Models and Algorithms,” Lecture Notes in Artificial Intelligence, 2037, pp. 1-8, Springer-Verlag, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 19, 2024