| Abstrakt: |
High-utility item-set mining has become a very crucial topic of research in recent times. It is used to find profitable products utilizing both profit factors and the quantity as opposed to the association rule mining or the frequent item-set mining. There are many algorithms that are proposed for mining, such high-utility item-sets, and almost all algorithms will need to grip an exponential search space for learning the high-utility item-sets. To handle the exponential search space, metaheuristic algorithms are used to find optimal solutions. Here, an optimization method depending on the grey wolf optimization and dolphin echolocation-optimization has been proposed for finding high-utility item-sets. The grey wolf optimization algorithm can be an interesting one owing to the strategy of group foraging. All conventional grey wolf optimization will deal with the problems of continuous optimization. In dolphin echolocation-optimization, as soon as the dolphin manages to reach the food, the search is reduced, and there is an increasing click made that directs food to the site where food is. The procedure also feigns dolphin echolocation by means of preventive exploration, and this is relational its target distance. In connection to the problems in optimization, there can be a particular features-set, which include progress of various hunting preys that sing echo sounding among dolphins, those having problems in answering. A hybrid dolphin echolocation-optimization (hybrid dolphin echolocation-binary grey wolf optimization) is proposed with a novel mechanism for finding global solutions. In this work, hybrid dolphin echolocation-binary grey wolf optimization algorithm is proposed. The dolphin echolocation-optimization had taken as it can be a promising solution and the Boolean format of grey wolf optimization is used. It works as a global search solution to distinguish the high-utility item-sets. The results prove that the dolphin echolocation-binary grey wolf optimization proposed approach has a greater percentage in high-utility item-sets discovery managing different types of datasets (mushroom, chess, accident, and connect datasets) instead of the other methods. Results show that the proposed DE-BGWO algorithm has a greater count of HUI for chess data convergence at 14.6 minutest utility threshold by 8.54%, by 14.51%, by 20.53% and by 20.93% for GA when compared with 200, 400, 600 and 800 iteration number respectively. |
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