DADApy: Distance-based analysis of data-manifolds in Python

DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main func...

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Detalles Bibliográficos
Autores principales: Glielmo, Aldo, Macocco, Iuri, Doimo, Diego, Carli, Matteo, Zeni, Claudio, Wild, Romina, d’Errico, Maria, Rodriguez, Alex, Laio, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583186/
https://www.ncbi.nlm.nih.gov/pubmed/36277821
http://dx.doi.org/10.1016/j.patter.2022.100589
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author Glielmo, Aldo
Macocco, Iuri
Doimo, Diego
Carli, Matteo
Zeni, Claudio
Wild, Romina
d’Errico, Maria
Rodriguez, Alex
Laio, Alessandro
author_facet Glielmo, Aldo
Macocco, Iuri
Doimo, Diego
Carli, Matteo
Zeni, Claudio
Wild, Romina
d’Errico, Maria
Rodriguez, Alex
Laio, Alessandro
author_sort Glielmo, Aldo
collection PubMed
description DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.
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spelling pubmed-95831862022-10-21 DADApy: Distance-based analysis of data-manifolds in Python Glielmo, Aldo Macocco, Iuri Doimo, Diego Carli, Matteo Zeni, Claudio Wild, Romina d’Errico, Maria Rodriguez, Alex Laio, Alessandro Patterns (N Y) Descriptor DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license. Elsevier 2022-09-19 /pmc/articles/PMC9583186/ /pubmed/36277821 http://dx.doi.org/10.1016/j.patter.2022.100589 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Descriptor
Glielmo, Aldo
Macocco, Iuri
Doimo, Diego
Carli, Matteo
Zeni, Claudio
Wild, Romina
d’Errico, Maria
Rodriguez, Alex
Laio, Alessandro
DADApy: Distance-based analysis of data-manifolds in Python
title DADApy: Distance-based analysis of data-manifolds in Python
title_full DADApy: Distance-based analysis of data-manifolds in Python
title_fullStr DADApy: Distance-based analysis of data-manifolds in Python
title_full_unstemmed DADApy: Distance-based analysis of data-manifolds in Python
title_short DADApy: Distance-based analysis of data-manifolds in Python
title_sort dadapy: distance-based analysis of data-manifolds in python
topic Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583186/
https://www.ncbi.nlm.nih.gov/pubmed/36277821
http://dx.doi.org/10.1016/j.patter.2022.100589
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