一种基于约束的多维数据异常点挖掘方法  

A Constraint-Based Multi-Dimensional Data Exception Mining Approach

作　　者：李翠平[1] 李盛恩[1] 王珊[2] 杜小勇[2] 

机构地区：[1]中国科学院计算技术研究所,北京100080 [2]中国人民大学数据与知识工程研究所,北京100872

出　　处：《软件学报》2003年第9期1571-1577,共7页Journal of Software

基　　金：国家自然科学基金;国家高技术研究发展计划(863);国家重点基础研究发展规划(973)~~

摘　　要：数据中的异常点常常反映了企业经营中潜伏的问题或暗藏的商机,数据分析人员经常需要从大量的数据中找出这些异常点.最近提出的一种从数据中自动发现异常点的方法,将人们从繁重的体力劳动中解放出来.然而,该方法在计算效率和伸缩性方面还存在很多不足.针对这些不足,对该方法进行了优化和改进,提出了一种基于约束的多维数据异常点挖掘方法.通过在数据挖掘过程中引入约束条件,首先将数据立方体限制到一个小的多维空间,然后再从中找出异常点.实验结果表明该方法非常有效.Data exceptions often reflect potential problems or dangers in the management of corporation. Analysts often need to identify these exceptions from large amount of data. A recent proposed approach automatically detects and marks the exceptions for the user and reduces the reliance on manual discovery. However, the efficiency and scalability of this method are not so satisfying. According to these disadvantages, the optimizations are investigated to improve it. A new method that pushes several constraints into the mining process is proposed in this paper. By enforcing several user-defined constraints, this method first restricts the multidimensional space to a small constrained-cube and then mines exceptions on it. Experimental results show that this method is efficient and scalable.

关 键 词：联机分析处理 数据挖掘 约束 异常点 实体化 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]