Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지

An Efficient Search Method for High Confidence Association Rules Using CP(Confidence Pattern)-Tree Structure

CP-Tree구조를 이용한 높은 신뢰도를 갖는 연관 규칙의 효율적 탐색 방법

  • 송한규 (한양대학교 산업공학과) ;
  • 김재련 (한양대학교 산업공학과)
  • Published : 2002.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

The traditional approaches of association rule mining have relied on high support condition to find interesting rules. However, in some application such as analyzing the web page link and discovering some unusual combinations of some factors that have always caused some disease, we are interested in rules with high confidence that have very low support or need not have high support. In these cases, the traditional algorithms are not suitable since it relies on first satisfying high support. In this paper, we propose a new model, CP(Confidence Pattern)-Tree, to identify high confidence rule between 2-items without support constraint. constraint. In addition, we discuss confidence association rule between two more items without support constraint.

Keywords

References

  1. R. Agrawal and R. Srikant, 'Fast Algorithm for Mining Association Rules in Large Databases', In Proc. of the 20th Int'l Conference on VLDB, pp. 487-499, eptember 1994
  2. R. Bayrardo Jr., 'Efficiently Mining Long Patterns from Database', In Proc. of the ACM SIGMOD Int'l Conference on management of Data, pp. 85-93, June 1998
  3. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, et al, 'Finding Interesting Associations without Support Pruning', IEEE Transactions on Knowledge and Data Engineering, 13, pp 64-78, 2001 https://doi.org/10.1109/69.908981
  4. S. Fujiwara, J. D. Ullman, R. Motwani, 'Dynamic Miss-Counting Algorithms: Finding Implication and Similarity Rules with Confidence Pruning', Proc. of the 16th ICDE. 2000
  5. B. Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, 1999
  6. J. Han, J. Pei, Y. Yin., 'Mining Frequent Patterns without Candidate Generation', Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data(SIGMOD'00), May 2000
  7. J. Li, X. Zhang, G. Dong, K. Ramamohanarao, and Q. Sun, 'Efficient Mining of High Confidence Association Rules without Support Thresholds', Proc. of 3rd European Conference on Principles and Practice of Knowledge Discovery in Database, September 1999
  8. M. Ogihara and M.J.Zaki 'Theoretical foundations of association rules'. In 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, June 1998
  9. J. Park, M. Chen, and P. Yu, 'An Effective Hash-Based Algorithm for Mining Association Rules', In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, pp.175-186, May 1995
  10. P. Pei, J. Han, and R. Mao, 'Closet: an efficient algorithm for mining frequent losed itemsets', Proc. 2000 ACM-SIGMOD Int. Workshop on Data Mining and Knowledge Discovery(DMKD'00), May 2000
  11. W. Perrizo, Qin Ding, Qiang Ding, and A. Roy, 'Deriving High Confidence Rules from Spatial Data using Peano Count Trees', Proc. of the 2nd International Conference 2001
  12. K. Wang, S. Zhou, Y. He, 'Growing Decision Trees on Support-less Association Rules', KDD, August 2000
  13. J. Zaki, Ching-Jui Hsiao, 'CHARM: An Efficient Algorithm for Closed Association Rule Mining', RPI Technical Report 99-10, 1999