Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A New Interestingness Measure in Association Rules Mining

연관규칙 탐색에서 새로운 흥미도 척도의 제안

  • Ahn, Kwang-Il (Department of Industrial Engineering, Hanyang University) ;
  • Kim, Seong-Jip (Department of Industrial Engineering, Hanyang University)
  • 안광일 (한양대학교 산업공학과) ;
  • 김성집 (한양대학교 산업공학과)
  • Published : 2003.03.31
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Abstract

In this paper, we present a new measure to evaluate the interestingness of association rules. Ultimately. to evaluate whether a rule is interesting or not is subjective. However, an interestingness measure is useful in that it shows the cause for pruning uninteresting rules statistically or logically. Some interestingness measures have been developed in association rules mining. We present an overview of interestingness measures and propose a new measure. A comparative study of some interestingness measures is made on an example dataset and a real dataset. Our experiments show that the new measure can avoid the discovery of misleading rules.

Keywords

References

  1. Agrawal, R., Imielinski, T. and Swami, A. N. (1993), Mining Association Rules between Sets of Items in Large Databases, Proc. ACM SIGMOD Int. Conf. on Management of Data, 207-216
  2. Agrawal, R. and Srikant, R.(1994), Fast Algorithms for Mining Association Rules in Large Databases, Proc. 20th Int. Conf. on Very Large Data Bases, 487-499
  3. Bayardo, R. J. (1998), Efficiently Mining Long Patterns from Databases, Proc. ACM SIGMOD Int. Conf. on Management of Data, 85-93
  4. Berry, M. J. A. and Linoff, G. (1997), Data Mining Techniques for Marketing. Sales. and Customer Support, John Wiley & Sons, Inc.
  5. Berzal, F., Blanco, I., Sanchez, D. and Vila, M. A. (2001), A New Framework to Assess Association Rules, Proc. 4th Int. Conf. on Intelligent Data Analysis, 95-104
  6. Brin, S., Motwani, R., Ullman, 1. D. and Tsur, S. (1997), Dynamic Itemset Counting and Implication Rules for Market Basket Data, Proc. ACM SIGMOD Int. Conf. on Management of Data, 255-264
  7. Cai, C. H., Fu, A. W. c., Cheng, C. H. and Kwong, W. W. (1998), Mining Association Rules with Weighted Items, Proc. Int. Database Engineering and Applications Symposium, 68-77
  8. Han, J. and Fu, Y. (1995), Discovery of Multiple-Level Association Rules from Large Databases, Proc. 21st Int. Conf. on Very Large Data Bases, 420-431
  9. Han, J. and Fu, Y. (1999), Mining Multiple-Level Association Rules in Large Databases, IEEE Transactions on Knowledge and Data Engineering, 11(5),798-804 https://doi.org/10.1109/69.806937
  10. Han, J., Pei, J. and Yin, Y. (2000), Mining Frequent Patterns without Candidate Generation, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1-12
  11. Kim, C-O., Ahn, K-I., Kim, S-J. and Kim, J-Y. (2001), An Efficient Tree Structure Method for Mining Association Rules, Journal of the Korean Institute of Industrial Engineers, 27(1), 30-36
  12. Liu, B., Hsu, W. and Ma, Y. (1998), Integrating Classification and Association Rule Mining, Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining. 80-86.(The CBA system can be downloaded from http://www.comp.nus.edu.sg/~dm2)
  13. Liu, B., Hsu, W. and Ma, Y. (1999), Mining Association Rules with Multiple Minimum Supports, Proc. 5th Int. Conf. on Knowledge Discovery and Data Mining, 337-341
  14. Park, J. S., Chen, M. S. and Yu, P. S.(1995), An Effective Hash Based Algorithm for Mining Association Rules, Proc. ACM SIGMOD Int. Conf. on Management of Data, 175-186
  15. Pasquier, N., Bastide, Y., Taouil, R. and Lakhal, L. (1999), Discovering Frequent Closed Itemsets tor Association Ruls, Proc. 7th Int. Conf. on Database Theory, 398-416
  16. Pei, J., Han, J. and Mao, R. (2000), CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets, ACMSIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 21-30
  17. Pialetsky-Shapiro, G. (1991), Discovery, Analysis and Presentation of Strong Rules, Knowledge Discovery in Databases, 229-248
  18. Silberschatz, A. and Tuzhilin, A. (1995), On Subjective Measures of Interestingness in Knowledge Discovery, Proc. 1st Int. Conf. on Knowledge Discovery and Data Mining, 275-281
  19. Silberschatz, A. and Tuzhilin, A. (1996), What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, 8(6), 970-974 https://doi.org/10.1109/69.553165
  20. Silverstein, c., Brin, S. and Motwani, R. (1998), Beyond Market Baskets: Generalizing Association Rules to Dependence Rules, Data Mining and Knowledge Discovery, 2(1), 39-68 https://doi.org/10.1023/A:1009713703947
  21. Srikant, R. and Agrawal, R. (1995), Mining Generalized Association Rules, Proc. 21st Int. Conf. on Very Large Data Bases, 407-419
  22. Tan, P-N., Kumar, V. and Srivastava, J. (2002), Selecting the Right Interestingness Measure for Association Patterns, Proc. 8th Int. Conf. on Knowledge Discovery and Data Mining