r an automated technique for clustering trajectory data using a Particle Swarm Optimization (PSO) and Dynamic Time Warping (DTW) distance measures for trajectory data, is able to find (near) optimal number
of clusters as well as (near) optimal cluster centers during the clustering process. To reduce the dimensionality of
the search space and improve the performance of the proposed method (in terms of a certain performance index),
a Discrete Cosine Transform (DCT) representation of cluster centers is considered.
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