This paper presents a simple and effective density-based outlier detection approach with local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure 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.
Recently, a relative density-based outlier score (RDOS) method involving the multivariate Gaussian kernel function was introduced, and it measured the local outlierness of objects with reference to their CNN REF .
A local density-based approach for outlier detection
Neurocomputing
Neurocomputing
Mathematics Computer Science