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Indexed by:会议论文
Date of Publication:2009-07-25
Included Journals:EI、CPCI-S、Scopus
Volume:2
Page Number:448-451
Key Words:data mining; outlier; local isolation coefficient
Abstract: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.