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Trajectory classification tasks became more complex as large volumes ofmobility data are being generated every day and enriched with new sources ofinformation, such as social networks and IoT sensors. Fast classificationalgorithms are essential for discovering knowledge in trajectory data for realapplications. In this work we propose a method for fast discovery ofsubtrajectories with the reduction of the search space and the optimization ofthe MASTERMovelets method, which has proven to be effective for discoveringinterpretable patterns in classification problems.

Fast discovery of multidimensional subsequences for robust trajectory classification