Benchmark and application of unsupervised classification approaches for univariate data

In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering...

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Autores principales:	Maria El Abbassi, Jan Overbeck, Oliver Braun, Michel Calame, Herre S. J. van der Zant, Mickael L. Perrin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/142f18d37da342038eb2d52cf28bd1d9
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id	oai:doaj.org-article:142f18d37da342038eb2d52cf28bd1d9
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spelling	oai:doaj.org-article:142f18d37da342038eb2d52cf28bd1d92021-12-02T13:30:41ZBenchmark and application of unsupervised classification approaches for univariate data10.1038/s42005-021-00549-92399-3650https://doaj.org/article/142f18d37da342038eb2d52cf28bd1d92021-03-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00549-9https://doaj.org/toc/2399-3650In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering method, and number of clusters for the analysis of a range of experimental datasets, including break-junction traces, I-V curves, and Raman spectra.Maria El AbbassiJan OverbeckOliver BraunMichel CalameHerre S. J. van der ZantMickael L. PerrinNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Maria El Abbassi Jan Overbeck Oliver Braun Michel Calame Herre S. J. van der Zant Mickael L. Perrin Benchmark and application of unsupervised classification approaches for univariate data
description	In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering method, and number of clusters for the analysis of a range of experimental datasets, including break-junction traces, I-V curves, and Raman spectra.
format	article
author	Maria El Abbassi Jan Overbeck Oliver Braun Michel Calame Herre S. J. van der Zant Mickael L. Perrin
author_facet	Maria El Abbassi Jan Overbeck Oliver Braun Michel Calame Herre S. J. van der Zant Mickael L. Perrin
author_sort	Maria El Abbassi
title	Benchmark and application of unsupervised classification approaches for univariate data
title_short	Benchmark and application of unsupervised classification approaches for univariate data
title_full	Benchmark and application of unsupervised classification approaches for univariate data
title_fullStr	Benchmark and application of unsupervised classification approaches for univariate data
title_full_unstemmed	Benchmark and application of unsupervised classification approaches for univariate data
title_sort	benchmark and application of unsupervised classification approaches for univariate data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/142f18d37da342038eb2d52cf28bd1d9
work_keys_str_mv	AT mariaelabbassi benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata AT janoverbeck benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata AT oliverbraun benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata AT michelcalame benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata AT herresjvanderzant benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata AT mickaellperrin benchmarkandapplicationofunsupervisedclassificationapproachesforunivariatedata
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Benchmark and application of unsupervised classification approaches for univariate data

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