The paper deals with the clustering of trajectories of moving objects. A k-means-like algorithm based on a Euclidean distance between piece-wise linear curves is used. The main novelty of the paper is the opportunity of considering in the clustering procedure a step that automatically weights the importance of subtrajectories of the original ones. The algorithm uses an adaptive distances approach and a cluster-wise weighting. The proposed algorithm is tested against some workbench trajectory datasets.

Trajectory clustering using adaptive squared distances

A. irpino
Writing – Original Draft Preparation
2019

Abstract

The paper deals with the clustering of trajectories of moving objects. A k-means-like algorithm based on a Euclidean distance between piece-wise linear curves is used. The main novelty of the paper is the opportunity of considering in the clustering procedure a step that automatically weights the importance of subtrajectories of the original ones. The algorithm uses an adaptive distances approach and a cluster-wise weighting. The proposed algorithm is tested against some workbench trajectory datasets.
2019
9788891915108
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/411857
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