An overview of high utility itemsets mining methods based on intelligent optimization algorithms

Mining high utility itemsets from massive data is one of the most active research directions in data mining at present. Intelligent optimization algorithms have been applied to the high utility itemsets mining because of their flexibility and intelligence, and have achieved good results. In this paper, high utility itemsets mining strategies based on swarm intelligence optimization algorithms are mainly analyzed and summarized comprehensively, and the strategies based on the evolutionary algorithms and other intelligence optimization algorithms are introduced in detail. The method based on swarm intelligence optimization algorithm is summarized and compared from the aspects of update strategy, pruning strategy, comparison algorithms, dataset, parameter settings, advantages, disadvantages, etc. The methods based on particle swarm optimization are classified in terms of particle update, which are traditional update strategies, sigmoid function-based strategies, greed-based strategies, roulette mechanism-based strategies, and set-based strategies. The experimental comparative analysis of the algorithms is carried out in terms of the operational efficiency of the algorithms and the number of high utility itemsets mined by the algorithms under the conditions of the same dataset. The experimental analysis shows that the strategy based on the swarm intelligence optimization algorithm is optimal, especially the high utility itemsets mining algorithm based on the bionic algorithm, which has a shorter running time and less number of high utility itemsets lost, and the least efficient strategy based on the genetic algorithm, which will lose a large number of itemsets.