A Survey on metaheuristic nature inspired computations used for Mining of Association Rule, Frequent Itemset and High Utility Itemset

Abstract

Metaheuristics are dilemma-independent methods that are generalizedin a variety of problems. In the real world, various problems are solved using generalized dilemma-independent methods called Metaheuristics Computation. Metaheuristic Nature Inspired Computing (MNIC) is a generalized approach to solve NP-hard problems by taking inspirations from the behavior of mother biological nature and their characteristics. Mining of Association rule, Frequent Itemset and High Utility Itemset are strongly interrelated and developing in the field of Data Mining. Metaheuristic nature inspired computation was widely used for the mining association rules of frequentitemsets and high utility itemsets to address the high computation time and optimal solutions. While various articles have been written, there is no systematic review of contemporary metaheuristic nature inspired approaches used in Association Rule Mining (ARM), Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM). This paper explores recent literature on various metaheuristics nature inspired approaches used for ARM, FIM and HUIM.