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dc.contributor.authorOliver, Elia
dc.contributor.authorVallés Pérez, Iván
dc.contributor.authorBaños, Rosa Maria
dc.contributor.authorCebolla Marti, Ausias
dc.contributor.authorBotella, Cristina
dc.contributor.authorSoria Olivas, Emilio
dc.date.accessioned2016-12-14T11:39:09Z
dc.date.available2016-12-14T11:39:09Z
dc.date.issued2016-08
dc.identifier.citationOLIVER, Elia, et al. Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis. Sociological Methods & Research, 2016.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/165043
dc.description.abstractData collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data set. Parallel coordinates and Chernoff faces are good examples of this. This article presents self-organizing maps (SOM), a multivariate visual data mining technique used to provide global visualizations of all the data. This technique is presented as a tutorial with the aim of showing its capabilities, how it works, and how to interpret its results. Specifically, SOM analysis has been applied to analyze the data collected in a study on the efficacy of a cognitive and behavioral treatment (CBT) for childhood obesity. The objective of the CBT was to modify the eating habits and level of physical activity in a sample of children with overweight and obesity. Children were randomized into two treatment conditions: CBT traditional procedure (face-to-face sessions) and CBT supported by a web platform. In order to analyze their progress in the acquisition of healthier habits, self-register techniques were used to record dietary behavior and physical activity. In the traditional CBT condition, children completed the self-register using a paper-and-pencil procedure, while in the web platform condition, participants completed the self-register using an electronic personal digital assistant. Results showed the potential of SOM for analyzing the large amount of data necessary to study the acquisition of new habits in a childhood obesity treatment. Currently, the high prevalence of childhood obesity points to the need to develop strategies to manage a large number of data in order to design procedures adapted to personal characteristics and increase treatment efficacy.ca_CA
dc.description.sponsorShipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded in part by the Spanish Ministry of Education, Culture and Sport, Projects ACTIOBE (PSI2011-25767), Excellence in Research Program PROMETEO II (Generalitat Valenciana. Conselleria de Educación, 2013/003), and CIBER Fisiopatología de la Obesidad y la Nutrición (ISC III CB06 03/0052).ca_CA
dc.format.extent15 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSageca_CA
dc.relation.isPartOfSociological Methods & Research, August 2016ca_CA
dc.rightsCopyright © 2016 by SAGE Publicationsca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectdata scienceca_CA
dc.subjectvisual data miningca_CA
dc.subjectself-organizing mapsca_CA
dc.subjecttutorialca_CA
dc.subjectartificial neural networksca_CA
dc.titleVisual data mining with self-organizing maps for "self-monitoring" data analysisca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1177/0049124116661576
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttp://journals.sagepub.com/doi/full/10.1177/0049124116661576ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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