Clustering of architectural floor plans: A comparison of shape representations
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/42051 https://doi.org/10.1016/j.autcon.2017.03.017 |
Resumo: | Generative design methods are able to produce a large number of potential solutions of architectural floor plans, which may be overwhelming for the decision-maker to cope with. Therefore, it is important to develop tools which organise the generated data in a meaningful manner. In this study, a comparative analysis of four architectural shape representations for the task of unsupervised clustering is presented. Three of the four shape representations are the Point Distance, Turning Function, and Grid-Based model approaches, which are based on known descriptors. The fourth proposed representation, Tangent Distance, calculates the distances of the contour's tangents to the shape's geometric centre. A hierarchical agglomerative clustering algorithm is used to cluster a synthetic dataset of 72 floor plans. When compared to a reference clustering, despite good perceptual results with the use of the Point Distance and Turning Function representations, the Tangent Distance descriptor (Rand index of 0.873) provides the best results. The Grid-Based descriptor presents the worst results. |
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Clustering of architectural floor plans: A comparison of shape representationsUnsupervised clusteringFloor plan designsHierarchical clusteringShape representationDescriptorsGenerative design methods are able to produce a large number of potential solutions of architectural floor plans, which may be overwhelming for the decision-maker to cope with. Therefore, it is important to develop tools which organise the generated data in a meaningful manner. In this study, a comparative analysis of four architectural shape representations for the task of unsupervised clustering is presented. Three of the four shape representations are the Point Distance, Turning Function, and Grid-Based model approaches, which are based on known descriptors. The fourth proposed representation, Tangent Distance, calculates the distances of the contour's tangents to the shape's geometric centre. A hierarchical agglomerative clustering algorithm is used to cluster a synthetic dataset of 72 floor plans. When compared to a reference clustering, despite good perceptual results with the use of the Point Distance and Turning Function representations, the Tangent Distance descriptor (Rand index of 0.873) provides the best results. The Grid-Based descriptor presents the worst results.Elsevier2017-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/42051http://hdl.handle.net/10316/42051https://doi.org/10.1016/j.autcon.2017.03.017https://doi.org/10.1016/j.autcon.2017.03.017eng0926-5805http://www.sciencedirect.com/science/article/pii/S0926580517302601Rodrigues, EugénioSousa-Rodrigues, DavidTeixeira de Sampayo, MafaldaGaspar, Adélio RodriguesGomes, ÁlvaroAntunes, Carlos Henggelerinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-07-22T08:22:21Zoai:estudogeral.uc.pt:10316/42051Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:58:36.712783Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Clustering of architectural floor plans: A comparison of shape representations |
title |
Clustering of architectural floor plans: A comparison of shape representations |
spellingShingle |
Clustering of architectural floor plans: A comparison of shape representations Rodrigues, Eugénio Unsupervised clustering Floor plan designs Hierarchical clustering Shape representation Descriptors |
title_short |
Clustering of architectural floor plans: A comparison of shape representations |
title_full |
Clustering of architectural floor plans: A comparison of shape representations |
title_fullStr |
Clustering of architectural floor plans: A comparison of shape representations |
title_full_unstemmed |
Clustering of architectural floor plans: A comparison of shape representations |
title_sort |
Clustering of architectural floor plans: A comparison of shape representations |
author |
Rodrigues, Eugénio |
author_facet |
Rodrigues, Eugénio Sousa-Rodrigues, David Teixeira de Sampayo, Mafalda Gaspar, Adélio Rodrigues Gomes, Álvaro Antunes, Carlos Henggeler |
author_role |
author |
author2 |
Sousa-Rodrigues, David Teixeira de Sampayo, Mafalda Gaspar, Adélio Rodrigues Gomes, Álvaro Antunes, Carlos Henggeler |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Rodrigues, Eugénio Sousa-Rodrigues, David Teixeira de Sampayo, Mafalda Gaspar, Adélio Rodrigues Gomes, Álvaro Antunes, Carlos Henggeler |
dc.subject.por.fl_str_mv |
Unsupervised clustering Floor plan designs Hierarchical clustering Shape representation Descriptors |
topic |
Unsupervised clustering Floor plan designs Hierarchical clustering Shape representation Descriptors |
description |
Generative design methods are able to produce a large number of potential solutions of architectural floor plans, which may be overwhelming for the decision-maker to cope with. Therefore, it is important to develop tools which organise the generated data in a meaningful manner. In this study, a comparative analysis of four architectural shape representations for the task of unsupervised clustering is presented. Three of the four shape representations are the Point Distance, Turning Function, and Grid-Based model approaches, which are based on known descriptors. The fourth proposed representation, Tangent Distance, calculates the distances of the contour's tangents to the shape's geometric centre. A hierarchical agglomerative clustering algorithm is used to cluster a synthetic dataset of 72 floor plans. When compared to a reference clustering, despite good perceptual results with the use of the Point Distance and Turning Function representations, the Tangent Distance descriptor (Rand index of 0.873) provides the best results. The Grid-Based descriptor presents the worst results. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-08 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/42051 http://hdl.handle.net/10316/42051 https://doi.org/10.1016/j.autcon.2017.03.017 https://doi.org/10.1016/j.autcon.2017.03.017 |
url |
http://hdl.handle.net/10316/42051 https://doi.org/10.1016/j.autcon.2017.03.017 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0926-5805 http://www.sciencedirect.com/science/article/pii/S0926580517302601 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133877619392512 |