Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules

Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text an...

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Detalles Bibliográficos
Autores: Vives Mestres, Marina, Kenett, Ron S., Thió i Fernández de Henestrosa, Santiago, Martín Fernández, Josep Antoni
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/19751
Acceso en línea:http://hdl.handle.net/10256/19751
Access Level:acceso abierto
Palabra clave:Mineria de regles d’associació
Association rule mining
Mineria de dades
Data mining
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spelling Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association RulesVives Mestres, MarinaKenett, Ron S.Thió i Fernández de Henestrosa, SantiagoMartín Fernández, Josep AntoniMineria de regles d’associacióAssociation rule miningMineria de dadesData miningAssociation rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to generate new visualizations and insights from association rule mining. These CoDa methods show the relationship between itemsets, their strength, and direction of dependency. Moreover, after expressing each association rule as a contingency table, we discuss two statistical tests to guide identification of the relevant rules by analyzing the relative importance of the elements of the table. As an example, we use these visualizations and statistical tests for investigating the association of negative mood emotions to various types of headache/migraine events. Data for those analysis comes from N1-HeadacheTM, a digital platform where individual users record attacks and symptoms as well as their daily exposure to a list of potential factorsThis research has been supported by theSpanish Ministry of Economy, Industry and Competitiveness under the project CODAMET (Ref: RTI2018-095518-B-C21)Open Access funding provided thanks to the CRUE-CSIC agreement with WileyWileyAgencia Estatal de Investigación2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/19751http://hdl.handle.net/10256/19751Quality and Reliability Engineering International, 2022, vol. 38, núm. 3, p. 1327-1339Articles publicats (D-IMA)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1002/qre.2910info:eu-repo/semantics/altIdentifier/issn/0748-8017info:eu-repo/semantics/altIdentifier/eissn/1099-1638RTI2018-095518-B-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095518-B-C21Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/197512026-05-29T05:05:01Z
dc.title.none.fl_str_mv Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
title Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
spellingShingle Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
Vives Mestres, Marina
Mineria de regles d’associació
Association rule mining
Mineria de dades
Data mining
title_short Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
title_full Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
title_fullStr Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
title_full_unstemmed Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
title_sort Measurement, selection, and visualization of association rules: A compositional data perspective: A Compositional Data perspective on Association Rules
dc.creator.none.fl_str_mv Vives Mestres, Marina
Kenett, Ron S.
Thió i Fernández de Henestrosa, Santiago
Martín Fernández, Josep Antoni
author Vives Mestres, Marina
author_facet Vives Mestres, Marina
Kenett, Ron S.
Thió i Fernández de Henestrosa, Santiago
Martín Fernández, Josep Antoni
author_role author
author2 Kenett, Ron S.
Thió i Fernández de Henestrosa, Santiago
Martín Fernández, Josep Antoni
author2_role author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Mineria de regles d’associació
Association rule mining
Mineria de dades
Data mining
topic Mineria de regles d’associació
Association rule mining
Mineria de dades
Data mining
description Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to generate new visualizations and insights from association rule mining. These CoDa methods show the relationship between itemsets, their strength, and direction of dependency. Moreover, after expressing each association rule as a contingency table, we discuss two statistical tests to guide identification of the relevant rules by analyzing the relative importance of the elements of the table. As an example, we use these visualizations and statistical tests for investigating the association of negative mood emotions to various types of headache/migraine events. Data for those analysis comes from N1-HeadacheTM, a digital platform where individual users record attacks and symptoms as well as their daily exposure to a list of potential factors
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/19751
http://hdl.handle.net/10256/19751
url http://hdl.handle.net/10256/19751
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/qre.2910
info:eu-repo/semantics/altIdentifier/issn/0748-8017
info:eu-repo/semantics/altIdentifier/eissn/1099-1638
RTI2018-095518-B-C21
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095518-B-C21
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Quality and Reliability Engineering International, 2022, vol. 38, núm. 3, p. 1327-1339
Articles publicats (D-IMA)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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