基于关联分析的高维空间异常点发现  

Discovery of High Dimensional Outliers Based on Association Analysis

作　　者：陆介平[1] 倪巍伟[1] 孙志挥[1] 

机构地区：[1]东南大学计算机科学与工程系,江苏南京210096

出　　处：《应用科学学报》2006年第1期60-63,共4页Journal of Applied Sciences

基　　金：国家自然科学基金(70371015);教育部高等学校博士学科点专项科研基金(20040286009);江苏省自然科学基金(BK2004058);国家科技部中小型企业创新基金(02C26213210070)资助项目

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.该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.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 intrusion detection systems, 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.

关 键 词：异常点 关联规则 闭频繁项集 k关系邻域 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]