Effective discovery of exception class association rules

In this paper, a new effective method is proposed to find class association 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 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 larger part of CARs are spurious and maybe misleading to users; the other is that some important ECARs 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.