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计算机工程 ›› 2018, Vol. 44 ›› Issue (12): 202-207,214. doi: 10.19678/j.issn.1000-3428.0049174

• 人工智能及识别技术 • 上一篇    下一篇

基于双重二元粒子群优化的高效用项集挖掘算法

靳晓乐,刘峡壁,马骁   

  1. 北京理工大学 智能信息技术北京市重点实验室,北京 100081
  • 收稿日期:2017-11-03 出版日期:2018-12-15 发布日期:2018-12-15
  • 作者简介:靳晓乐(1992—),男,硕士研究生,主研方向为高效用项集挖掘;刘峡壁,副教授、博士;马骁,硕士研究生

High-utility Itemsets Mining Algorithm Based on Double Binary Particle Swarm Optimization

JIN Xiaole,LIU Xiabi,MA Xiao   

  1. Beijing Laboratory of Intelligent Information Technology,Beijing Institute of Technology,Beijing 100081,China
  • Received:2017-11-03 Online:2018-12-15 Published:2018-12-15

摘要:

高效用项集挖掘算法是关联分析中的重要组成部分,通过对基本二元粒子群算法进行改进,提出一种双重二元粒子群优化(DBPSO)算法。运用最小相对效用阈值和效用上界的乘积确定最小效用阈值。利用最小效用阈值和适应度函数分散候选子空间,挖掘高效用项集。实验结果表明,该算法的收敛速度较快,能够获得较多的高效用项集。

关键词: 高效用项集, 双重二元粒子群优化, 最小效用阈值, 效用上界, 分散子空间

Abstract:

High-utility itemset mining algorithm is an important part of association analysis.By improving the basic binary particle swarm optimization algorithm,a Double Binary Particle Swarm Optimization(DBPSO) algorithm is proposed.The minimum utility threshold is determined using the product of the minimum relative utility threshold and the utility upper bound.The candidate subspace is dispersed by the minimum utility threshold and fitness function,and the high-utility itemset is mined.Experimental results show that compared with the UP-Growth algorithm,the algorithm has a faster convergence rate and can mine a higher high-utility itemset.

Key words: high-uitility itemsets, Double Binary Particle Swarm Optimization(DBPSO), minimum utility threshold, utility upper bound, splitting subspace

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