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Hierarchical Quick Shift Guided Recurrent Clustering

MCML Authors

Christian Böhm

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

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.

inproceedings