Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and main challenge has achieve a balance between search efficiency quality mined rules. As novel algorithm, water wave optimization (WWO) algorithm widely used for combinatorial problems, with disadvantage that it tends fall into local optimum solutions converges slowly. this paper, hybrid ARM method based on WWO Levy flight (LWWO) proposed. The proposed improves solution by expanding space through while increasing speed. addition, paper employs strategy enhance diversity population order obtain global optimal solution. Moreover, does not generate frequent items, unlike traditional (e.g., Apriori), thus reducing computational overhead saving memory space, which increases its applicability real-world business cases. Experiment results show performance significantly better than LWWO terms number