Analysis of clustering and selection algorithms for the study of multivariate wave climate

Recent wave reanalysis databases require the application of techniques capable of managing huge amounts of information. In this paper, several clustering and selection algorithms: K-Means (KMA), self-organizing maps (SOM) and Maximum Dissimilarity (MDA) have been applied to analyze trivariate hourly...

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Autores: Camus Braña, Paula, Méndez Incera, Fernando Javier|||0000-0002-5005-1100, Medina Santamaría, Raúl|||0000-0002-0126-2710, Cofiño González, Antonio Santiago|||0000-0001-7719-979X
Tipo de recurso: artículo
Fecha de publicación:2011
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/35093
Acceso en línea:https://hdl.handle.net/10902/35093
Access Level:acceso abierto
Palabra clave:Data mining
K-means
Maximum dissimilarity algorithm
Probability density function
Reanalysis database
Self-organizing maps
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spelling Analysis of clustering and selection algorithms for the study of multivariate wave climateCamus Braña, PaulaMéndez Incera, Fernando Javier|||0000-0002-5005-1100Medina Santamaría, Raúl|||0000-0002-0126-2710Cofiño González, Antonio Santiago|||0000-0001-7719-979XData miningK-meansMaximum dissimilarity algorithmProbability density functionReanalysis databaseSelf-organizing mapsRecent wave reanalysis databases require the application of techniques capable of managing huge amounts of information. In this paper, several clustering and selection algorithms: K-Means (KMA), self-organizing maps (SOM) and Maximum Dissimilarity (MDA) have been applied to analyze trivariate hourly time series of met-ocean parameters (significant wave height, mean period, and mean wave direction). A methodology has been developed to apply the aforementioned techniques to wave climate analysis, which implies data pre-processing and slight modifications in the algorithms. Results show that: a) the SOM classifies the wave climate in the relevant "wave types" projected in a bidimensional lattice, providing an easy visualization and probabilistic multidimensional analysis; b) the KMA technique correctly represents the average wave climate and can be used in several coastal applications such as longshore drift or harbor agitation; c) the MDA algorithm allows selecting a representative subset of the wave climate diversity quite suitable to be implemented in a nearshore propagation methodology.The work was partially funded by projects “GRACCIE” (CSD2007-00067, CONSOLIDER-INGENIO 2010) from the Spanish Ministry MICIN, “MARUCA” from the Spanish Ministry MF and “C3E” from the Spanish Ministry MAMRM. The authors thank Puertos del Estado (Spanish Ministry MF) for the use of the reanalysis data base.ElsevierUniversidad de Cantabria20112011-06-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/35093Coastal Engineering, 2011, 8(6), 453-462reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/350932026-06-02T12:39:31Z
dc.title.none.fl_str_mv Analysis of clustering and selection algorithms for the study of multivariate wave climate
title Analysis of clustering and selection algorithms for the study of multivariate wave climate
spellingShingle Analysis of clustering and selection algorithms for the study of multivariate wave climate
Camus Braña, Paula
Data mining
K-means
Maximum dissimilarity algorithm
Probability density function
Reanalysis database
Self-organizing maps
title_short Analysis of clustering and selection algorithms for the study of multivariate wave climate
title_full Analysis of clustering and selection algorithms for the study of multivariate wave climate
title_fullStr Analysis of clustering and selection algorithms for the study of multivariate wave climate
title_full_unstemmed Analysis of clustering and selection algorithms for the study of multivariate wave climate
title_sort Analysis of clustering and selection algorithms for the study of multivariate wave climate
dc.creator.none.fl_str_mv Camus Braña, Paula
Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Medina Santamaría, Raúl|||0000-0002-0126-2710
Cofiño González, Antonio Santiago|||0000-0001-7719-979X
author Camus Braña, Paula
author_facet Camus Braña, Paula
Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Medina Santamaría, Raúl|||0000-0002-0126-2710
Cofiño González, Antonio Santiago|||0000-0001-7719-979X
author_role author
author2 Méndez Incera, Fernando Javier|||0000-0002-5005-1100
Medina Santamaría, Raúl|||0000-0002-0126-2710
Cofiño González, Antonio Santiago|||0000-0001-7719-979X
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Data mining
K-means
Maximum dissimilarity algorithm
Probability density function
Reanalysis database
Self-organizing maps
topic Data mining
K-means
Maximum dissimilarity algorithm
Probability density function
Reanalysis database
Self-organizing maps
description Recent wave reanalysis databases require the application of techniques capable of managing huge amounts of information. In this paper, several clustering and selection algorithms: K-Means (KMA), self-organizing maps (SOM) and Maximum Dissimilarity (MDA) have been applied to analyze trivariate hourly time series of met-ocean parameters (significant wave height, mean period, and mean wave direction). A methodology has been developed to apply the aforementioned techniques to wave climate analysis, which implies data pre-processing and slight modifications in the algorithms. Results show that: a) the SOM classifies the wave climate in the relevant "wave types" projected in a bidimensional lattice, providing an easy visualization and probabilistic multidimensional analysis; b) the KMA technique correctly represents the average wave climate and can be used in several coastal applications such as longshore drift or harbor agitation; c) the MDA algorithm allows selecting a representative subset of the wave climate diversity quite suitable to be implemented in a nearshore propagation methodology.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/35093
url https://hdl.handle.net/10902/35093
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Coastal Engineering, 2011, 8(6), 453-462
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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