A Recursive Algorithm for Mining Association Rules

TL;DR

This paper introduces PrefRec, a recursive algorithm for mining association rules that efficiently updates when data changes, demonstrated through comparative experiments on execution time and update performance.

Contribution

The paper presents a novel recursive algorithm, PrefRec, for mining association rules that improves update efficiency and is thoroughly analyzed and tested against existing methods.

Findings

PrefRec outperforms traditional algorithms in execution time.

PrefRec effectively updates with new or removed items.

Experimental results confirm its efficiency and adaptability.

Abstract

Mining frequent itemsets and association rules is an essential task within data mining and data analysis. In this paper, we introduce PrefRec, a recursive algorithm for finding frequent itemsets and association rules. Its main advantage is its recursiveness with respect to the items. It is particularly efficient for updating the mining process when new items are added to the database or when some are excluded. We present in a complete way the logic of the algorithm, and give some of its applications. After that, we carry out an experimental study on the effectiveness of PrefRec. We first compare the execution times with some very popular frequent itemset mining algorithms. Then, we do experiments to test the updating capabilities of our algorithm.