Generalized k-means-based clustering for temporal data under weighted and kernel time warp

This presentation is about clustering temporal data under time warp measures, a challenging problem as it requires aligning multiple temporal data simultaneously. To circumvent this problem, costly k-medoids and kernel k-means algorithms are generally used. This work investigates a different approach to temporal data clustering through weighted and kernel time warp measures and a tractable and fast estimation of the barycentre of the clusters that captures both global and local temporal features. A wide range of public challenging datasets, encompassing images, traces and ecg data that are non-isotropic (i.e., non-spherical), not well-isolated and linearly non-separable, is used to evaluate the efficiency of the proposed temporal data clustering.