Weighted Association Rule Discovery for Item Groups with Different Properties

상이한 특성을 갖는 아이템 그룹에 대한 가중 연관 규칙 탐사

  • 김정자 (전남대학교 자연과학대학 컴퓨터 정보학부) ;
  • 정희택 (여수대학교 인터넷 전산정보학과)
  • Published : 2004.10.01

Abstract

In market-basket analysis, weighted association rule(WAR) discovery can mine the rules which include more beneficial information by reflecting item importance for special products. However, when items are divided into more than one group and item importance for each group must be measured by different measurement or separately, we cannot directly apply traditional weighted association rule discovery. To solve this problem, we propose a novel methodology to discovery the weighted association rule in this paper In this methodology, the items should be first divided into sub-groups according to the properties of the items, and the item importance is defined or calculated only with the items enclosed to the sub-group. Our algorithm makes qualitative evaluation for network risk assessment possible by generating risk rule set for risk factor using network sorority data, and quantitative evaluation possible by calculating risk value using statistical factors such as weight applied in rule generation. And, It can be widely used for new model of more delicate analysis in market-basket database in which the data items are distinctly separated.

References

  1. Feng Tao, Fionn Murtagh, Mohsen Farid 'Weighted Association Rule Mining using Weighted Support and Significance Framework', SIGKDD 2003
  2. Feng Tao, 'Mining Binary Relationships from Transaction Data in Weighted Settings', PhD Thesis, School of Computer Science, Queen's University Belfast, UK, 2003
  3. W. Wang, J. Yang P. Yu, 'Efficient Mining of Weighted Association Rules(WAR)', Prec. of the ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, 2000, pp
  4. C.H. Cai, Ada W.C. Fu, C.H. Cheng and W.W. Kwong, 'Mining Association Rules with Weighted Items' International Database Engineering and Application Symposium, 1998
  5. G.D.Ramkumar, Sanjay Ranka, and Shalom Tsur, 'Weighted Association Rules : Model and Algorithm', KDD 1998
  6. Jiawei Han and Yongjian Fu, 'Discovery of Multiple-Level Association Rules from Large Databases' in the Proceedings of the 1995 Int'l Conf. on Very Large Data Bases(VLDB'95), Zurich, Switzerland, 2002, pp. 420-431
  7. N.Pasquier, Y.Bastide, R.Taouil, and L.Lakhal, 'Efficient Mining of Association Rules using Closed Itemset Lattices', Information Systems, Vol. 24, No.1, 1999, pp. 25-46 https://doi.org/10.1016/S0306-4379(99)00003-4
  8. E. -H. Han, G. Karypis, and V. Kumar, 'Scalable Parallel Data Mining for Association Rules', Proc. ACM SIGMOD, Tucson, U.S.A., 1997, pp. 277-288
  9. A. Savasere, E. Omiencinsky , and S. Navathe, 'An efficient algorithm for mining association rules in large databases', In Proceedings of the 21st VLDB Conference, pp.432-444, Zurich, Swizerland, 1995
  10. R. SriKant and R. Agrawal, 'Mining Generalized Association Rules', In Proceedings of the 21st VLDB conference, Zurich, Swizerland, 1995
  11. M. Klemettinen, H. Mannila, P.Ronkainen, H.Toivonen, and A. I. Verkamo, 'Finding Interesting Rules from Large Sets of Discovered Association Rules', Proc. of the 3rd IntI. Conf. on Information and Knowledge Management, 1994, pp. 401-407