Efficient Remining of Generalized Multi-supported Association Rules under Support Update

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in data mining. In our previous work, we have investigated this problem and proposed two algorithms, MMS_Cumulate and MMS_Stratify. In real applications, however, the work of discovering interesting association rules is an iterative process. The analysts need to repeatedly adjust the constraint of minimum support and/or minimum confidence to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examined the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is updated. Empirical evaluation showed that the proposed RM_MMS algorithm is very efficient and has good linear scale-up characteristic. Key-Words: Data remining, generalized association rules, multiple minimum supports, taxonomy.