Affiliations: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
Correspondence:
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Corresponding author. Meng Han, School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China. E-mails: [email protected]; [email protected].
Note: [1] This work was supported by the National Natural Science Foundation of China (62062004) and the Natural Science Foundation of Ningxia (2023AAC03315).
Abstract: The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.
Keywords: Beluga whale optimization algorithm, harris hawk optimization algorithm, high utility itemsets mining, good point set, intelligent optimization algorithm
