计算机工程 ›› 2011, Vol. 37 ›› Issue (19): 68-70.doi: 10.3969/j.issn.1000-3428.2011.19.021

• 软件技术与数据库 • 上一篇    下一篇

改进的数据流频繁闭项集挖掘算法

李国栋,胡建平   

  1. (天津城市建设学院电子与信息工程系,天津 300384)
  • 收稿日期:2011-03-25 出版日期:2011-10-05 发布日期:2011-10-05
  • 作者简介:李国栋(1980-),男,讲师,主研方向:数据挖掘,虚拟现实;胡建平,教授
  • 基金项目:
    国家科技支撑计划基金资助项目(2008BAJ08B16)

Improved Mining Algorithm for Frequent Closed Itemsets over Data Stream

LI Guo-dong, HU Jian-ping   

  1. (Dept. of Electronic & Information Engineering, Tianjin Institute of Urban Construction, Tianjing 300384, China)
  • Received:2011-03-25 Online:2011-10-05 Published:2011-10-05

摘要: NewMoment算法在数据挖掘过程中频繁地进行左检测操作,导致算法运行效率低下。针对该问题,提出一种改进的数据流频繁闭项集挖掘算法——LevelMoment。在该算法中,给出一种新的加入层次节点的数据结构LevelCET,在此结构上通过层次检测策略与最佳频繁闭项集检测策略,快速地挖掘数据流滑动窗口中的所有频繁闭项集。实验结果表明,改进算法在运行时间与存储空间上性能较优。

关键词: 数据流, 频繁闭项集, 滑动窗口, 层次检测策略, 最佳频繁闭项集检测策略

Abstract: Aiming at the problem of NewMoment algorithm frequently do leftcheck operation in the data mining process, which leads to the low efficiency of algorithm, this paper proposes an improved method called LevelMoment to improve the NewMoment algorithm which mines frequent closed itemsets over data streams. In this process, a new data structure that added in level node, called LevelCET, is proposed. On this structure, using level checking strategy and optimum frequent closed items checking strategy can quickly tap all the frequent closed itemsets over data streams. Experimental results show that the algorithm has good performance on run time and storage space.

Key words: data stream, frequent closed itemset, sliding window, level checking strategy, optimum frequent closed itemsets checking strategy

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