Visual data mining with self-organizing maps for ''self-monitoring'' data analysis
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Visual data mining with self-organizing maps for ''self-monitoring'' data analysis

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Visual data mining with self-organizing maps for ''self-monitoring'' data analysis

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dc.contributor.author Oliver Gasch, Elia
dc.contributor.author Vallés Pérez, Iván
dc.contributor.author Baños Rivera, Rosa María
dc.contributor.author Cebolla i Martí, Ausiàs Josep
dc.contributor.author Botella Arbona, Cristina
dc.contributor.author Soria Olivas, Emilio
dc.date.accessioned 2017-02-08T15:27:46Z
dc.date.available 2017-02-08T15:27:46Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/10550/57094
dc.description.abstract Data 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.
dc.language.iso eng
dc.relation.ispartof Sociological Methods & Research, 2016, p. 1-15
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Oliver, Elia Vallés Pérez, Iván Baños Rivera, Rosa María Cebolla i Martí, Ausiàs Josep Botella Arbona, Cristina Soria Olivas, Emilio 2016 Visual data mining with self-organizing maps for ''self-monitoring'' data analysis Sociological Methods & Research 1 15
dc.subject Personalitat sociopatològica
dc.subject Psicologia
dc.title Visual data mining with self-organizing maps for ''self-monitoring'' data analysis
dc.type info:eu-repo/semantics/article
dc.date.updated 2017-02-08T15:27:47Z
dc.identifier.doi http://dx.doi.org/10.1177/0049124116661576
dc.identifier.idgrec 115382

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