Visual data mining with self-organizing maps for "self-monitoring" data analysis
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Scholar |
Altres documents de l'autoria: Oliver, Elia; Vallés Pérez, Iván; Baños, Rosa Maria; Cebolla Marti, Ausias; Botella, Cristina; Soria Olivas, Emilio
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https://doi.org/10.1177/0049124116661576 |
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Títol
Visual data mining with self-organizing maps for "self-monitoring" data analysisAutoria
Data de publicació
2016-08Editor
SageCita bibliogràfica
OLIVER, Elia, et al. Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis. Sociological Methods & Research, 2016.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://journals.sagepub.com/doi/full/10.1177/0049124116661576Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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 ... [+]
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. [-]
Publicat a
Sociological Methods & Research, August 2016Drets d'accés
Copyright © 2016 by SAGE Publications
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info:eu-repo/semantics/restrictedAccess
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