High utility itemset mining using dolphin echolocation optimization

Abstract

The critical issue identified in recent times is the high utility itemset mining (HUIM). It may be used for showing products that are profitably utilizing considering the factors of profit and quality as opposed to the frequent itemset (FIM) or the association rule (ARM) mining. There are numerous high utility itemset mining (HUIs) algorithms, mostly designed for handling the exponential search space to discover the HUIs at the time the number of the distinct items and the database that was very large. For this purpose, a meta-heuristic algorithm is designed for HUIs mining, working based on the genetic algorithm (GA) and the Dolphin echolocation optimization (DEO). The intended purpose of this evolutionary computation (EC) techniques on the DEO, here only fewer parameters are required to be compared to the approaches that are based on the GA. As the traditional DEO mechanism had been found for handling this continuous problem, an efficient algorithm based on the DEO called the high-utility itemset mining-DEO (HUIM-DEO) was proposed. To prove that the algorithm proposed was able to outperform the other heuristic algorithms to mine the HUIs for the time taken for execution, the number of HUIs discovered, and their convergence.