2 research outputs found
A Local Density-Based Approach for Local Outlier Detection
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 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.Comment: 22 pages, 14 figures, submitted to Pattern Recognition Letter