Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information

Bayesian Biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [1] • It includes background knowledge directly into the clustering process. • It finds similarity between series and over time. • It allows a user-specifie...

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Detalles Bibliográficos
Autores principales: Pinto, Helen, Gates, Ian, Wang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Energy
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199012/
https://www.ncbi.nlm.nih.gov/pubmed/32382523
http://dx.doi.org/10.1016/j.mex.2020.100897
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author Pinto, Helen
Gates, Ian
Wang, Xin
author_facet Pinto, Helen
Gates, Ian
Wang, Xin
author_sort Pinto, Helen
collection PubMed
description Bayesian Biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [1] • It includes background knowledge directly into the clustering process. • It finds similarity between series and over time. • It allows a user-specified definition for behaviour of interest, which relaxes dependency on series shape. This is important when similar behavioural events do not necessarily occur in the same temporal order.
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spelling pubmed-71990122020-05-07 Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information Pinto, Helen Gates, Ian Wang, Xin MethodsX Energy Bayesian Biclustering by Dynamics (BBCD) is a new clustering algorithm for Steam-Assisted Gravity Drainage (SAGD) oil recovery time series data [1] • It includes background knowledge directly into the clustering process. • It finds similarity between series and over time. • It allows a user-specified definition for behaviour of interest, which relaxes dependency on series shape. This is important when similar behavioural events do not necessarily occur in the same temporal order. Elsevier 2020-04-22 /pmc/articles/PMC7199012/ /pubmed/32382523 http://dx.doi.org/10.1016/j.mex.2020.100897 Text en © 2020 The Author(s). Published by Elsevier B.V. http://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 Energy
Pinto, Helen
Gates, Ian
Wang, Xin
Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title_full Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title_fullStr Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title_full_unstemmed Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title_short Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
title_sort bayesian biclustering by dynamics: algorithm testing, comparison against random agglomeration, and calculation of application specific prior information
topic Energy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199012/
https://www.ncbi.nlm.nih.gov/pubmed/32382523
http://dx.doi.org/10.1016/j.mex.2020.100897
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