摘要
高效用项集挖掘(HUIM)能够挖掘事务数据库中具有重要意义的项集,从而帮助用户更好地进行决策。针对智能优化算法的应用能够显著提高海量数据中高效用项集的挖掘效率这一现状,对基于智能优化算法的HUIM方法进行了综述。首先,以智能优化算法的类别为角度,从基于群智能优化、基于进化以及基于其他智能优化算法的方法这3个方面对基于智能优化算法的HUIM方法进行了详细的分析与总结。同时,从粒子更新方式的角度对基于粒子群优化(PSO)的HUIM方法进行了详细梳理,包括基于传统更新策略、基于sigmoid函数、基于贪心、基于轮盘赌以及基于集合的方法。另外,从种群更新方法、对比算法、参数设置、优缺点等角度对比分析了基于群智能优化算法的HUIM方法。然后,从遗传和仿生两个方面对基于进化的HUIM方法进行总结概括。最后,针对目前基于智能优化算法的HUIM方法所存在的问题,提出了下一步的研究方向。
High Utility Itemsets Mining(HUIM)is able to mine the items with high significance from transaction database,thus helping users to make better decisions.In view of the fact that the application of intelligent optimization algorithms can significantly improve the mining efficiency of high utility itemsets in massive data,a survey of intelligent optimization algorithm-based HUIM methods was presented.Firstly,detailed analysis and summary of the intelligent optimization algorithm-based HUIM methods were performed from three aspects:swarm intelligence optimization-based,evolution-based and other intelligent optimization algorithms-based methods.Meanwhile,the Particle Swarm Optimization(PSO)-based HUIM methods were sorted out in detail from the aspect of particle update methods,including traditional update strategy-based,sigmoid function-based,greedy-based,roulette-based and ensemble-based methods.Additionally,the swarm intelligence optimization algorithm-based HUIM methods were compared and analyzed from the perspectives of population update methods,comparison algorithms,parameter settings,advantages and disadvantages,etc.Next,the evolution-based HUIM methods were summarized and outlined in terms of both genetic and bionic aspects.Finally,the next research directions were proposed for the problems of the existing intelligent optimization algorithm-based HUIM methods.
作者
高智慧
韩萌
刘淑娟
李昂
穆栋梁
GAO Zhihui;HAN Meng;LIU Shujuan;LI Ang;MU Dongliang(School of Computer Science and Engineering,North Minzu University,Yinchuan Ningxia 750021,China)
出处
《计算机应用》
CSCD
北大核心
2023年第6期1676-1686,共11页
journal of Computer Applications
基金
国家自然科学基金资助项目(62062004)
宁夏自然科学基金资助项目(2020AAC03216)。
关键词
高效用项集挖掘
智能优化算法
粒子群优化算法
进化算法
启发式算法
High Utility Itemsets Mining(HUIM)
intelligent optimization algorithm
Particle Swarm Optimization(PSO)algorithm
Evolutionary Algorithm(EA)
heuristic algorithm
