计算机科学 ›› 2011, Vol. 38 ›› Issue (12): 187-190.

• 数据库与数据挖掘 • 上一篇    下一篇

均衡时空挖掘数据流中频繁项集

宋奎勇,任永功,寇香霞   

  1. (辽宁师范大学计算机与信息技术学院 大连116029)
  • 出版日期:2018-12-01 发布日期:2018-12-01

Balanced Space-time Frequent Itemsets Mining over Data Stream

  • Online:2018-12-01 Published:2018-12-01

摘要: 数据流具有流动性、连续性以及项分布不均衡性等特点,挖掘数据流中频繁项集是一项意义重大且具有挑战性的工作。提出一种均衡时空挖掘数据流中频繁项集算法—Bala_ Tree, Bala_ Tree实现一遍扫描数据流、快速簇更新、周期树结构重构以及基于经典算法挖掘频繁项集。实验表明,此算法能快速扫描和更新数据,合理利用内存以及精确获得频繁项集,Ba1a_Tree算法优于其他同类算法。

关键词: 数据流,频繁项集,均衡,Bala_Tree

Abstract: Data stream has characteristics of the flow, continuity, and the unbalanced distribution of item Minging frequent itemsets over data stream is a significant and challenging work. Presented a balanced space-time algorithm for mining frequent itemsets over data stream-Bala_Tree. The algorithm can only scan data stream once, make rapid cluster updates, periodical tree reconstruction and mine frequent itemsets based on classical algorithm. Experiments show that the algorithm can quickly scan and update data, realize the rational use of memory, accurate access to frectuent itemsets. Bala_ Tree algorithm is superior to other algorithms.

Key words: Data stream, Frequent itemsets, Balance, Bala_ Tree

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