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.