@inproceedings{ac5d95ffb311460fa152e4e2a769a835,
title = "Gauss Shift: Density Attractor Clustering Faster Than Mean Shift",
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.",
keywords = "Clustering, Density attractor clustering, Efficiency, Gauss shift, Local search, Neuroscience, Optimization, mean shift",
author = "Richard Leibrandt and Stephan G{\"u}nnemann",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 ; Conference date: 14-09-2020 Through 18-09-2020",
year = "2021",
doi = "10.1007/978-3-030-67658-2_8",
language = "English",
isbn = "9783030676575",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "125--142",
editor = "Frank Hutter and Kristian Kersting and Jefrey Lijffijt and Isabel Valera",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings",
}