【摘要】 高效用项集挖掘是数据挖掘中发现数据之间关系的技术之一.例如在商业服务领域效用值代表了某些商品组合的利润.高效用项集挖掘能够挖掘出数据中效用值比较大的项集—高效用项集,因此高效用项集挖掘近年来受到了更多的关注与研究.针对高效用项集在数据集中的不均匀分布,提出一个改进的粒子群优化高效用项集挖掘算法.算法改进了粒子群优化流程中种群优化值的生成方式,通过轮盘赌选择法在当前代种群的高效用项集中以一定概率选择下一代种群的初始优化值.这个改进增加了种群的多样性,使得算法能够挖掘出更多的高效用项集.实验结果验证了算法的可行性和有效性.更多还原

【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.更多还原