An Efficient Approach for Incremental Association Rule Mining

Abstract

In this paper, we study the issue of maintaining association rules in a large database of sales transactions. The maintenance of association rules can be mapped into the problem of maintaining large itemsets in the database. Because the mining of association rules is time-consuming, we need an efficient approach to maintain the large itemsets when the database is updated. In this paper, we present efficient approaches to solve the problem. Our approaches store the itemsets that are not large at present but may become large itemsets after updating the database, so that the cost of processing the updated database can be reduced. Moreover, we discuss the cases where the large itemsets can be obtained without scanning the original database. Experimental results show that our algorithms outperform other algorithms, especially when the original database need not be scanned in our algorithms.