In order to solve the density based outlier detection problem with low accuracy and high computation, a variance of distance and density (VDD) measure is proposed in this paper. And the k-means clustering and score based VDD (KSVDD) approach proposed can efficiently detect outliers with high performance. For illustration, two real-world datasets are utilized to show the feasibility of the approach. Empirical results show that KSVDD has a good detection precision.
A Fast Trajectory Outlier Detection Approach via Driving Behavior Modeling
