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窗口模式下在线数据流中频繁项集的挖掘 被引量：1

Online data stream mining of recent frequent itemsets based on sliding window model
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摘要 拟采用一种基于滑动窗模式的单遍挖掘算法,专注于处理近期数据;为了减少处理时间和占用的内存,设计了一种新的事务表示方法。通过处理这个事务的表达式,频繁项集可以被高效输出,并解决了使用基于Apriori理论的算法时,由候选频繁1-项集生成频繁2-项集时数据项顺序判断不准确问题。该算法称为MRFI-SW算法。 This paper proposed a one-pass data stream mining algorithm to mine the recent frequent itemsets in data streams with a sliding window based on transactions.To reduce the cost of time and memory needed to slide the windows,denoted each items a bit-sequence representations. Basing on dealing with the representations,can find frequent patterns in data streams efficiently,and the sequent of frequent 2-items is correct.This paper named the method MRFI-SW（mining recent frequent itemsets by sliding window）algorithm.
作者 李可 任家东
机构地区 燕山大学信息科学与工程学院
出处 《计算机应用研究》 CSCD 北大核心 2010年第5期1711-1713,共3页 Application Research of Computers
基金 国家"863"计划资助项目(2009AA01Z433) 河北省自然科学基金资助项目(F2008000888)
关键词 在线数据流 频繁项集 滑动窗 online data stream frequent items sliding window
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献8

  • 1MANKU G S,MOTWANI R.Approximate frequency counts over data streams[C]//Proc of the 28th International Conference on Very Large Data Bases.2002:346-357.
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  • 4CHANG J,LEE W.A sliding window method for finding recently frequent itemsets over online data streams[J].Journal of Information Science and Engineering,2004,20(4):175-184.
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同被引文献9

  • 1牛小飞,石冰,卢军,吴科.挖掘关联规则的高效ABM算法[J].计算机工程,2004,30(11):118-120. 被引量：16
  • 2BABCOCK B,BABU S,DATAR M, et al. Models and issues in data stream systems [ C ]//Proc of the 21 st ACM SIGMOD-SIGART Sympo- sium on Principles of Database System. New York:ACM Press,2002: 1-16.
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  • 4LEE D, LEE W. Finding maximal frequent itemsets over online data streams adaptively [ C ]//Proc of the 5th IEEE International Confe- rence on Daia Mining. Washington DC : IEEE Computer Society,2005 : 266 - 273.
  • 5LI Hua-fu, LEE S, SHAN M. Online mining maximal frequent itemsets over data streams[ C]//Proc of the 15th International Workshops on Research Issues in Data Engineering: Stream Data Mining and Appli- cations. 2005 : 11 - 18.
  • 6MAO Guo-jun, WU Xin-dong, ZHU Xing-quan, et al. Mining maximal frequent itemsets from data streams[ J]. Journal of Information Sci- ence,2007,33(3 ) :251-262.
  • 7GIANNELLA C, HAN Jia-wei, PEI Jian, et al. Mining frequent pat- terns in data streams at multiple time granularities [ M ]//Next Gene- ration Data Mining. Cambridge : MIT Press ,2005 : 191 - 212.
  • 8BORGELT C. Keeping things simple:finding frequent itemsets by re- cursive elimination [ C ]//Proc of the 1 st International Workshop on Open Source Data Mining. New York :ACM Press,2005:66-70.
  • 9AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[ C]//Proc of the 20th International Conference on Very Large Databases. San Francisco: Morgan Kaufmann Publishers, 1994:487- 499.

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计算机应用研究
2010年 第5期

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