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Gauss Shift: Density Attractor Clustering Faster Than Mean Shift

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12457))

Abstract

Mean shift is a popular and powerful clustering method. While techniques exist that improve its absolute runtime, no method has been able to effectively improve its quadratic time complexity with regard to dataset size. To enable development of an alternative, faster method that leads to the same results, we first contribute the formal cluster definition, which mean shift implicitly follows. Based on this definition we derive and contribute Gauss shift – a method that has linear time complexity. We quantify the characteristics of Gauss shift using synthetic datasets with known topologies. We further qualify Gauss shift using real-life data from active neuroscience research, which is the most comprehensive description of any subcellular organelle to date.

Supplementary material: www.daml.in.tum.de/gauss-shift.

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Authors and Affiliations

  1. Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany

    Richard Leibrandt & Stephan Günnemann

Authors
  1. Richard Leibrandt
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  2. Stephan Günnemann
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Corresponding author

Correspondence to Richard Leibrandt .

Editor information

Editors and Affiliations

  1. Albert-Ludwigs-Universität, Freiburg, Germany

    Frank Hutter

  2. TU Darmstadt, Darmstadt, Germany

    Kristian Kersting

  3. Ghent University, Ghent, Belgium

    Jefrey Lijffijt

  4. Saarland University, Saarbrücken, Germany

    Isabel Valera

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Leibrandt, R., Günnemann, S. (2021). Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12457. Springer, Cham. https://doi.org/10.1007/978-3-030-67658-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-67658-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67657-5

  • Online ISBN: 978-3-030-67658-2

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