Gauss Shift: Density Attractor Clustering Faster Than Mean Shift

Richard Leibrandt, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

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.

OriginalspracheEnglisch
TitelMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
Redakteure/-innenFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten125-142
Seitenumfang18
ISBN (Print)9783030676575
DOIs
PublikationsstatusVeröffentlicht - 2021
VeranstaltungEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Dauer: 14 Sept. 202018 Sept. 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12457 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

KonferenzEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
OrtVirtual, Online
Zeitraum14/09/2018/09/20

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