A Local Density-Based Approach for Local Outlier Detection
This work addresses outlier detection for data analysis applications, presenting an incremental improvement over existing density-based methods.
The paper tackles outlier detection by proposing a local density-based approach using a Relative Density-based Outlier Score (RDOS) with local kernel density estimation, extended nearest neighbors, and theoretical analysis, and demonstrates it outperforms state-of-the-art methods in experiments on synthetic and real-life datasets.
This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of using only $k$ nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. Some theoretical properties of the proposed RDOS including its expected value and false alarm probability are derived. A comprehensive experimental study on both synthetic and real-life data sets demonstrates that our approach is more effective than state-of-the-art outlier detection methods.