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基于关联分析的高维空间异常点发现

陆介平 倪巍伟 孙志挥   

  1. 东南大学 计算机科学与工程系, 江苏 南京 210096
  • 收稿日期:2004-09-27 修回日期:2004-12-14 出版日期:2006-01-31 发布日期:2006-01-31

Discovery of High Dimensional Outliers Based on Association Analysis

LU Jie-ping, NI Wei-wei, SUN Zhi-hui
  

  1. Department of Computer Science and Engineering, Southeast University, Nanjing 210096 China
  • Received:2004-09-27 Revised:2004-12-14 Online:2006-01-31 Published:2006-01-31

被引次数

21

摘要： 异常点发现是从大量数据对象中挖掘少量具有异常行为模式的数据对象，很多情况下，这些数据对象较之正常行为模式包含了更多用户感兴趣的信息。本文针对某些具体应用领域中的数据对象具有高维性的特点，利用关联分析知识，提出一种高维空间异常点发现算法，理论分析和实验表明，算法是有效可行的。

关键词: 异常点、关联规则、闭频繁项集、k关系邻域

Abstract:

Discovery of outliers is to extract a few data objects with abnormal behavior patterns, which are more interesting than common patterns in some cases, from a large amount of data. It is of practical significance in IDS, credit fraud detection, etc. Data in these domains are usually high dimensional, particularly featured by their sparseness and decline properties. An algorithm that can obtain the outliers with high efficiency is proposed based on association analysis. Effectiveness of the algorithm is shown by theory analysis and experiment results.

Key words:

outlier, association analysis, closed frequent item-sets, k-relational neighboring area