Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets

Abstract

Mining patterns from High-utility itemsets (HUIs) have been exploited recently in place of frequent itemset mining (FIMs) or association-rule mining (ARMs) as they highlight profitability of products where quantity and profits are taken into account. Several techniques for HUIs have been proposed and they encounter exponential search spaces which have more distinct items or voluminous databases. Alternatively, Evolutionary Computations (ECs)-based meta-heuristics algorithms can be effective in solving issues in HUIs since a set of near-optimal solutions can be obtained within restricted periods. Current ECs-based techniques consume more time to identify HUIs in transactional databases, discover unacceptable combinations of HUIs, and finally fail to discover HUIs when neighborhood searches are not executed locally and globally. To overcome these challenges, a HUI mining algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) using multiple strategies including elitism, population diversifications, exclusive preservations, and neighborhood exploration techniques has been proposed. Thus, this work defines mining patterns based on DE and PSO to identify HUIs in voluminous transactional databases. The HUIM-DE-PSO-DE algorithm proposed in this work discovers more number of HUIs which is revealed in experimental results obtained from a set of benchmark data instances. Results are compared with existing approaches using several performance metrics including convergence speeds, minimum utility threshold values, and execution time consumed.