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Journal of Computer Science and Technology

, Volume 17, Issue 3, pp 304–313 | Cite as

Effective discovery of exception class association rules

  • Zhou Aoying Email author
  • Wei Li 
  • Yu Fang 
Regular Papers
  • 44 Downloads

Abstract

In this paper, a new effective method is proposed to find class association rules (CAR), to getuseful class association rules (UCAR) by removing thespurious class association rules (SCAR), and to generateexception class association rules (ECAR) for each UCAR. CAR mining, which integrates the techniques of classification and association, is of great interest recently. However, it has two drawbacks: one is that a large part of CARs are spurious and may be misleading to users; the other is that some important ECARs are difficult to find using traditional data mining techniques. The method introduced in this paper aims to get over these flaws. According to our approach, a user can retrieve correct information from UCARs and know the influence from different conditions by checking corresponding ECARs. Experimental results demonstrate the effectiveness of our proposed approach.

Keywords

data mining class association rule exception class association rule pruning 

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Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2002

Authors and Affiliations

  1. 1.Department of Computer ScienceFudan UniversityShanghaiP.R. China

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