一种增强的局部异常挖掘方法  

An Enhanced Approach for Mining Local Outlier

作　　者：蒋盛益[1] 李庆华[1] 王卉[1] 孟中楼[1] 

机构地区：[1]华中科技大学计算机科学与技术学院

出　　处：《计算机研究与发展》2005年第2期210-216,共7页Journal of Computer Research and Development

基　　金：国家自然科学基金项目 (60 2 73 0 75 )

摘　　要：异常检测在许多领域有重要应用 在提出度量具有混合属性的对象间差异性方法的基础上 ,将加权幂平均引入数据挖掘 ,提出一种基于最近邻的异常检测方法 ,这种方法采用广义局部异常因子GLOF度量对象的异常程度 ,不需要阈值或数据集中异常数据个数的先验知识 理论分析表明 ,GLOF具有好的性质 实验表明 :①对象间差异性定义适合于混合属性的数据集 ;②GLOF比LOF ,CBLOF ,RNN更准确地刻画了局部异常 ;③“Bσ”In many cases, outliers are more important than the normal data, as they may demonstrate either deviant behavior, or the beginning of a new pattern, they may be cause damage to user. Outlier detection has become an important branch of data mining. In this paper, a new generalized method measuring the difference of two objects with mixed attributes is presented, and the weighted power mean is introduced to data mining. Based on these, a new outlier detection approach based on the nearest neighborhood is proposed. The approach measures outlier degree of an object by generalized local outlier factor (GLOF), and detects outlier by the rule of “Bσ”; also it needn't threshold or the prior knowledge about the number of outlier in dataset. GLOF generalizes LOF (local outlier factor) and COF (connectivity-based outlier factor). The theoretic analysis finds out some interesting properties of GLOF. The experimental results show that:(1) The definition about the difference of two objections can be used to dataset with mixed attributes. (2) In some cases GLOF measures the local outlier more accurately than LOF,CBLOF,RNN do. (3) The rule of “Bσ” is simple and promising in practice.

关 键 词：数据挖掘 异常检测 加权幂平均 “Bσ”准则 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]