一种基于相似度量的离群点检测方法

摘要

Outlier detection is an important content in data mining and is widely used in the field of credit card fraud detection, network invasion detection and so on. According to hierarchical clustering and similarity, this paper presents the concept of high dimensional data similarity measurement function and class density, based on class density,the outlier of high dimensional data is redefined so that a kind of outlier detection algorithm based on similarity measurement is proposed. Experiment shows that this algorithm has certain value on outlier detection in high dimensional data.%离群点检测在是数据挖掘的重要领域,广泛应用在信用卡欺诈检测、网络入侵检测等重要方面,文中在结合层次聚类和相似性,给出高维数据的相似度量函数与类密度的概念,并基于类密度重新定义高维数据的离群点,从而提出一种基于相似度量的离群点检测算法;实验表明：算法对高维数据中的离群点检测有一定的价值。