Warped K-means (WKM) is a segmentation-based partitional clustering procedure that minimizes the Sum of Squared Error criterion, while imposing a hard sequentiality constraint in the classification step. The algorithm is tailored to simplify trajectories of n-dimensional objects, and it has interesting properties. In particular, it is much faster than conventional K-means algorithms and at the same time it provides better solutions for sequentially-distributed data.
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(cc) 2021 Luis A. Leiva, Enrique Vidal