High Utility Itemset Mining Algorithm Based on Improved Particle Swarm Optimization
WANG Changwu1，YIN Songlin1，LIU Wenyuan1，WEI Xiaomei1 ，ZHENG Hongjun1，YANG Jiping2
1（School of Information Science and Engineering，Yanshan University，Qinhuangdao 066004，China）2（School of Robotics，Beijing Union University，Beijing 100101，China）
Abstract：High utility itemset mining is one of the techniques for discovering the relationship between data in data mining.Utility value represents the profit of certain commodity combinations in the field of business services.The high utility itemset mining can mine the itemsets with large utility value in the data.Therefore，high utility itemset mining has received more attention and research in recent years.Because the distribution of high utility itemsets are not uniform in the dataset，this paper proposes a high utility itemset mining algorithm based on improved particle swarm optimization.The algorithm changes the generation method of the population optimization value in the particle swarm optimization process.The roulette selection method is used to select the initial optimization value of the next generation population with a certain probability in the high high itemsets of the current generation population.This change increases the diversity of the population，allowing the algorithm to mine more high utility itemsets.Finally，the experimental results verify the feasibility and effectiveness of the algorithm.
2020, Vol. 41 
