A probability-based incremental association rule discovery algorithm for record insertion and deletion

Abstract

The maintenance of association rules for dynamic database is an important problem because the updates may not only invalidate some existing rules but also make other rules relevant. This paper is the extension work of probability-based incremental association rule discovery algorithm which can only handle new data insert into a dynamic database. Unlike the previous work, the proposed algorithm can efficiently handle in case of insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains the support counts of frequent itemsets and promising frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Promising frequent itemsets, which are obtained by using the principle of Bernoulli trials, can help to reduce a number of times to rescan the original database. The support counts of new candidate itemsets are approximated by using the principle of maximum possible value. The experimental results show that the execution time of the proposed algorithm is faster than that of Apriori, FUP2, EDUA, and pre-large algorithm.