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 AMB+20

---

ICDE 2020

36th IEEE International Conference on Data Engineering. Dallas, TX, USA, Apr 20-24, 2020.

---

Authors

M. C. Altinigneli • L. Miklautz • C. Böhm • C. Plant

---

Links

DOI

---

Research Area

 A3 | Computational Models