Context learning from a ship trajectory cluster for anomaly detection.

• Context learning extraction from ship trajectory data. • AIS data real-world data use for data mining problems and anomaly detection. • Trajectory compression and segmentation techniques. • Data mining techniques for trajectory clustering applications. This paper presents a context information extraction process over Automatic Identification System (AIS) real-world ship data, building a system with the capability to extract representative points of a trajectory cluster. With the trajectory cluster, the study proposes the use of trajectory segmentation algorithms to extract representative points of each trajectory and then use the k-means algorithm to obtain a series of centroids over all the representative points. These centroids, combined, form a new representative trajectory of the cluster. This new representative trajectory of the input cluster represents new contextual information extracted from the original set of trajectories, being possible to apply anomaly detection approaches over the new obtained context. The results show a suitable approach with several compression algorithms that are compared with a metric based on the Perpendicular Euclidean Distance. [ABSTRACT FROM AUTHOR]