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.File in questo prodotto:
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