一种基于约束的多维数据异常点挖掘方法

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.

A Constraint-Based Multi-Dimensional Data Exception Mining Approach

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.