Computation of frequent itemset using matrix based apriori algorithm

Abstract

The Apriori Algorithm is a traditional method for determining the frequent itemsets from a lot of data. Association rules can be generated based on frequently occurring itemsets. The Apriori algorithm has two bottlenecks: it generates a large number of candidate sets and repeatedly examines the database. It takes a long time to execute and takes up a lot of space. We provide a novel strategy called Matrix-Based Apriori Algorithm to overcome these problems. It is easy to implement but effective in handling the issues of Apriori. We don't need to constantly scan the database because all operations are first applied to the matrix, after which the database is converted back into its original form. In addition, we have reduced the potential itemsets by using several pruning techniques. The Matrix Based Apriori algorithm outperforms the standard Apriori algorithm in terms of time, with an average time reduction rate of 71.5% with the first experiment and 86% with the second. In a similar vein, we contrasted our Matrix Based Apriori with an effective alternative known as improved apriori. We discovered that our method outperforms the upgraded apriori by 20%.