A visual-numeric approach to clustering and anomaly detection for trajectory data

Abstract

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,03..