点击次数：

论文类型：会议论文

发表时间：2009-07-25

收录刊物：EI、CPCI-S、Scopus

卷号：2

页面范围：448-451

关键字：data mining; outlier; local isolation coefficient

摘要：Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions. Distance-based outlier detection is an important data mining technique that rinds abnormal data objects according to some distance function. However, when this technique is applied to datasets whose density distribution is different, usually the detection efficiency and result are not perfect. With analysis of features of outliers in datasets, as the improvement of Local Sparsity Coefficient-Based (LSC) Mining of Outliers, we rank each point on the basis of its distance to its kth nearest neighbor and the distribution of its k nearest neighborhood. A novel outlier detecting algorithm based Local Isolation Coefficient (LIC) is presented in this paper, which is shown better outlier mining results through the experiments.