Effective Discovery of Exception Class Association Rules

In this paper, a new effective method is proposed to find classassociation rules (CAR), to get useful class association rules(UCAR) by removing the spurious class association rules(SCAR),and to generate exception class association rules (ECAR) for eachUCAR. CAR mining, which integrates the techniques ofclassification and association, is of great interest recently. However,it has two drawbacks: one is that a large part of CARs arespurious and maybe misleading to users; the other is that some importantECARs are difficult to find using traditional data miningtechniques. The method introduced in this paper aims to get over theseflaws. According to our approach, a user can retrieve correct informationfrom UCARs and know the influence from different conditions bychecking corresponding ECARs. Experimental results demonstrate theeffectiveness of our proposed approach.