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计算机工程 ›› 2008, Vol. 34 ›› Issue (12): 4-6.doi: 10.3969/j.issn.1000-3428.2008.12.002

• 博士论文 • 上一篇    下一篇

基于记忆效应的局部异常检测算法

李 健1,2，阎保平1，李 俊1   

  1. (1. 中国科学院计算机网络信息中心，北京 100080；2. 中国科学院研究生院，北京 100080)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-06-20 发布日期:2008-06-20

Memory-effect-based Local Outlier Detection Algorithm

LI Jian1,2, YAN Bao-ping1, LI Jun1   

  1. (1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100080;2. Graduate University of Chinese Academy of Sciences, Beijing 100080)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-06-20 Published:2008-06-20

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1470

被引次数

21 | 4

摘要： 基于密度的局部异常检测算法(LOF算法)的时间复杂度较高，限制了其在高维数据集以及大规模数据集中的使用。该文通过分析LOF算法，引入记忆效应概念，提出具有记忆效应的局部异常检测算法——MELOF算法。实验测试表明，该算法的计算结果与LOF算法完全相同，而且能够大大缩短运行时间。

关键词: 数据挖掘, 异常检测, 局部异常因子, 记忆效应, MELOF算法

Abstract: The computational complexity of algorithm for identifying density-based local outliers (LOF algorithm) is not ideal, which affects its applications in large scale data sets, especially in high dimensional data sets. Under such circumstances, the concept of memory effect is introduced, which lays the foundation for the newly enhanced algorithm called MELOF. Experimental result shows that MELOF algorithm obtains the same result as LOF algorithm, and shortens the execution time obviously.

Key words: data mining, outlier detection, Local Outlier Factor(LOF), memory effect, MELOF algorithm

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