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Altinigneli, Muzaffer Can; Miklautz, Lukas; Bohm, Christian; Plant, Claudia (2020): Hierarchical Quick Shift Guided Recurrent Clustering. In: 2020 IEEE 36Th International Conference on Data Engineering (Icde 2020): pp. 1842-1845
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We propose a novel density-based mode-seeking Hierarchical Quick Shift clustering algorithm with an optional Recurrent Neural Network (RNN) to jointly learn the cluster assignments for every sample and the underlying dynamics of the mode-seeking clustering process. As a mode-seeking clustering algorithm, Hierarchical Quick Shift constrains data samples to stay on similar trajectories. All data samples converging to the same local mode are assigned to a common cluster. The RNN enables us to learn quasi-temporal structures during the mode-seeking clustering process. It supports variable density clusters with arbitrary shapes without requiring the expected number of clusters a priori. We evaluate our method in extensive experiments to show the advantages over other density-based clustering algorithms.