Abstract

Discovery of association rule is one of the most interesting areas of research in data mining, which extracts together occurrence of itemset. In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. It is important to study efficient algorithms for incremental update of association rules in large databases. In this paper, we modify an existing incremental algorithm, Probability-based incremental association rule discovery. The previous algorithm, probability-based incremental association rule discovery algorithm uses principle of Bernoulli trials to find frequent and expected frequent k-itemsets. The set of frequent and expected frequent k-itemsets are determined from a candidate k-itemsets. Generating and testing the set of candidate is a time-consuming step in the algorithm. To reduce the number of candidates 2-itemset that need to repeatedly scan the database and check a large set of candidate, our paper is utilizing a hash technique for the generation of the candidate 2-itemset, especially for the frequent and expected frequent 2-itemsets, to improve the performance of probability-based algorithm. Thus, the algorithm can reduce not only a number of times to scan an original database but also the number of candidate itemsets to generate frequent and expected frequent 2 itemsets. As a result, the algorithm has execution time faster than the previous methods. This paper also conducts simulation experiments to show the performance of the proposed algorithm. The simulation results show that the proposed algorithm has a good performance.