Sample-adaptive-parameters outlier detection method for associated-attributes
适用于关联属性的样本自适应参数孤立点检测法

In order to solve the interfering problem of associated-attributes in datasets, this paper improved the traditional k-nearest neighbor outlier detection method by the introduction of Mahalanobis distance, and proposed a new sample-based parameters selection method which gained the optimization k-distance value and threshold by training the normal and outlier data in the sample dataset. Simulation results illustrate the proposed algorithm has higher accuracy, lower false detection rate.