Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm

Abstract

The frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent. This is called the dynamic update problem of frequent itemsets and the solution is to devise an algorithm that can dynamically mine the frequent itemsets. In this study, a dynamic frequent itemset mining algorithm, which is called Dynamic Matrix Apriori, is proposed and explained. In addition, the proposed algorithm is compared using two datasets with the base algorithm Matrix Apriori which should be re-run when the database is updated.