A Prefixed-Itemset-Based Improvement For Apriori Algorithm

TL;DR

This paper introduces a prefixed-itemset data structure to enhance the efficiency of the classical Apriori algorithm in data mining for association rules.

Contribution

It proposes a novel prefixed-itemset-based data structure to improve candidate itemset generation in Apriori, addressing its efficiency bottleneck.

Findings

Significant reduction in computation time for candidate generation

Improved overall efficiency of Apriori algorithm

Potential for better scalability in large datasets

Abstract

Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.