G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth

Detalhes bibliográficos
Autor(a) principal: Lobo, João Pedro Pereira
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/48350
Resumo: Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020
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spelling G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truthTriclusteringDados tridimensionaisGeração de dados sintéticosAprendizagem não supervisionadaAvaliação de algoritmosTeses de mestrado - 2020Departamento de InformáticaTese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Three-dimensional datasets, or three-way data, started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations _ features _ contexts). With an increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount.These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real three-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. G-Tric can replicate real-world datasets and create new ones that match researchers’ needs across several properties, including data type (numeric or symbolic), dimension, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled by defining the number of missing values, noise, and errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches. Besides reviewing the current state-of-the-art regarding triclustering approaches, comparison studies and evaluation metrics, this work also analyzes how the lack of frameworks to generate synthetic data influences existent evaluation methodologies, limiting the scope of performance insights that can be extracted from each algorithm. As well as exemplifying how the set of decisions made on these evaluations can impact the quality and validity of those results. Alternatively, a different methodology that takes advantage of synthetic data with ground truth is presented. This approach, combined with the proposal of an extension to an existing clustering extrinsic measure, enables to assess solutions’ quality under new perspectives.Madeira, Sara Alexandra CordeiroRepositório da Universidade de LisboaLobo, João Pedro Pereira2021-12-30T01:30:19Z202020202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/48350TID:202599647enginfo: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:RCAAP2023-11-08T16:51:42Zoai:repositorio.ul.pt:10451/48350Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:00:15.389597Repositório 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 G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
title G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
spellingShingle G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
Lobo, João Pedro Pereira
Triclustering
Dados tridimensionais
Geração de dados sintéticos
Aprendizagem não supervisionada
Avaliação de algoritmos
Teses de mestrado - 2020
Departamento de Informática
title_short G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
title_full G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
title_fullStr G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
title_full_unstemmed G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
title_sort G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
author Lobo, João Pedro Pereira
author_facet Lobo, João Pedro Pereira
author_role author
dc.contributor.none.fl_str_mv Madeira, Sara Alexandra Cordeiro
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Lobo, João Pedro Pereira
dc.subject.por.fl_str_mv Triclustering
Dados tridimensionais
Geração de dados sintéticos
Aprendizagem não supervisionada
Avaliação de algoritmos
Teses de mestrado - 2020
Departamento de Informática
topic Triclustering
Dados tridimensionais
Geração de dados sintéticos
Aprendizagem não supervisionada
Avaliação de algoritmos
Teses de mestrado - 2020
Departamento de Informática
description Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020-01-01T00:00:00Z
2021-12-30T01:30:19Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/48350
TID:202599647
url http://hdl.handle.net/10451/48350
identifier_str_mv TID:202599647
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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