LSOF: Novel Outlier Detection Approach Based on Local Structure

Abstract

Many local outlier detection algorithms have been proposed inspired by the idea of local outlier factor (LOF). However, they often have low detection performance and are sensitive to neighborhood size because there is a major defect in their calculation formulas of outlier degree and the kNN (k-nearest neighbors) method is widely used to quantify a neighborhood of an instance. To address these issues, we define a novel nearest neighbors tree (NNT) to measure a neighborhood of an instance. Meanwhile, we propose a local structure outlier factor (LSOF), which score each local structure instead of each data point and report the top-scored local structures as anomalous local structures, where outliers and groups of outliers are easily divided according to characteristics of the NNT. Our experimental results demonstrate that the competitive behavior of our method on both synthetic and real-world datasets.