Publication
G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth
| Summary: | Three-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. |
|---|---|
| Main Authors: | Lobo, João Pedro Pereira |
| Subject: | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| Year: | 2020 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade de Lisboa |
| Language: | English |
| Origin: | Repositório da Universidade de Lisboa |
| _version_ | 1866811602130436096 |
|---|---|
| author | Lobo, João Pedro Pereira |
| author_facet | Lobo, João Pedro Pereira |
| author_role | author |
| contributor_name_str_mv | Madeira, Sara Alexandra Cordeiro Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Lobo, João Pedro Pereira\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Madeira, Sara Alexandra Cordeiro Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Lobo, João Pedro Pereira |
| datacite.date.Accepted.fl_str_mv | 2020-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-12-30T01:30:19Z |
| datacite.date.embargoed.fl_str_mv | 2021-12-30T01:30:19Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| datacite.titles.title.fl_str_mv | G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth |
| dc.contributor.none.fl_str_mv | Madeira, Sara Alexandra Cordeiro Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Lobo, João Pedro Pereira |
| dc.date.Accepted.fl_str_mv | 2020-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-12-30T01:30:19Z |
| dc.date.embargoed.fl_str_mv | 2021-12-30T01:30:19Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/48350 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| dc.title.fl_str_mv | G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | Three-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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/9f8fd914-5b53-4108-9e40-53d9f872b4e8/download |
| id | ul_b0db4ba6fc522f97cb79db276e9d8e13 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/48350 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/48350 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Lobo, João Pedro Pereira |
| publishDate | 2020 |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engpt_PTThree-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.application/pdfpt_PTG-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truthLobo, João Pedro PereiraMadeira, Sara Alexandra CordeiroHostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptURNurn:tid:2025996472021-12-30T01:30:19Z202020202020-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/48350http://purl.org/coar/access_right/c_abf2open accessTriclusteringDados tridimensionaisGeração de dados sintéticosAprendizagem não supervisionadaAvaliação de algoritmosTeses de mestrado - 20205807349 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/9f8fd914-5b53-4108-9e40-53d9f872b4e8/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| title | 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_short | 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 |
| topic | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| topic_facet | Triclustering Dados tridimensionais Geração de dados sintéticos Aprendizagem não supervisionada Avaliação de algoritmos Teses de mestrado - 2020 |
| url | http://hdl.handle.net/10451/48350 |
| visible | 1 |