Reptile Search Algorithm for Association Rule Mining

Abstract

Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.