Exploratory data analysis in the context of data mining and resampling.

Exploratory data analysis in the context of data mining and resampling.

Guardado en:

International Journal of Psychological Research

2011-2084

2011-7922

Ho Yu, Chong

UNIVERSIDAD DE SAN BUENAVENTURA

10.21500/20112084.819

3

2010-06-30

9

22

Colombia

http://purl.org/coar/access_right/c_abf2

info:eu-repo/semantics/openAccess

International Journal of Psychological Research - 2010

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Artículo de revista
Exploratory data analysis in the context of data mining and resampling.
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Altman, D. G., & Royston, P. (2000).What do we mean by validating a prognostic model? Statistics in Medicine, 19, 453-473. Baker, B. D., & Richards, C. E. (1999). A comparison of conventional linear regression methods and neural networks for forecasting educational spending. Economics of Education Review, 18, 405-415. Behrens, J. T. & Yu, C. H. (2003). Exploratory data analysis. In J. A. Schinka & W. F. Velicer, (Eds.), Handbook of psychology Volume 2: Research methods in Psychology (pp. 33-64). New Jersey: John Wiley & Sons, Inc. Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2, 131-160. Berk, R. A. (2008). Statistical learning from a regression perspective. New York: Springer. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Monterey, CA: Wadsworth International Group. Carpio, K.J.E. & Hermosilla, A.Y. (2002), On multicollinearity and artificial neural networks, Complexity International, 10, Retrieved October 8, 2009, from http://www.complexity.org.au/ci/vol10/hermos01/.
International Journal of Psychological Research - 2010
https://creativecommons.org/licenses/by-nc-sa/4.0/
Inglés
https://revistas.usb.edu.co/index.php/IJPR/article/view/819
International Journal of Psychological Research
Text
info:eu-repo/semantics/article
Universidad San Buenaventura - USB (Colombia)
Publication
neural networks
Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.
Ho Yu, Chong
exploratory data analysis
data mining
resampling
cross-validation
data visualization
clustering
classification trees
3
1
Núm. 1 , Año 2010 : Special Issue of Statistics in Psychology
Journal article
2010-06-30
9
22
https://doi.org/10.21500/20112084.819
https://revistas.usb.edu.co/index.php/IJPR/article/download/819/595
10.21500/20112084.819
2011-7922
2011-2084
2010-06-30T00:00:00Z
2010-06-30T00:00:00Z
institution UNIVERSIDAD DE SAN BUENAVENTURA
thumbnail https://nuevo.metarevistas.org/UNIVERSIDADDESANBUENAVENTURA_COLOMBIA/logo.png
country_str Colombia
collection International Journal of Psychological Research
title Exploratory data analysis in the context of data mining and resampling.
spellingShingle Exploratory data analysis in the context of data mining and resampling.
Ho Yu, Chong
neural networks
exploratory data analysis
data mining
resampling
cross-validation
data visualization
clustering
classification trees
title_short Exploratory data analysis in the context of data mining and resampling.
title_full Exploratory data analysis in the context of data mining and resampling.
title_fullStr Exploratory data analysis in the context of data mining and resampling.
title_full_unstemmed Exploratory data analysis in the context of data mining and resampling.
title_sort exploratory data analysis in the context of data mining and resampling.
description_eng Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.
author Ho Yu, Chong
author_facet Ho Yu, Chong
topic neural networks
exploratory data analysis
data mining
resampling
cross-validation
data visualization
clustering
classification trees
topic_facet neural networks
exploratory data analysis
data mining
resampling
cross-validation
data visualization
clustering
classification trees
citationvolume 3
citationissue 1
citationedition Núm. 1 , Año 2010 : Special Issue of Statistics in Psychology
publisher Universidad San Buenaventura - USB (Colombia)
ispartofjournal International Journal of Psychological Research
source https://revistas.usb.edu.co/index.php/IJPR/article/view/819
language Inglés
format Article
rights http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
International Journal of Psychological Research - 2010
https://creativecommons.org/licenses/by-nc-sa/4.0/
references_eng Altman, D. G., & Royston, P. (2000).What do we mean by validating a prognostic model? Statistics in Medicine, 19, 453-473. Baker, B. D., & Richards, C. E. (1999). A comparison of conventional linear regression methods and neural networks for forecasting educational spending. Economics of Education Review, 18, 405-415. Behrens, J. T. & Yu, C. H. (2003). Exploratory data analysis. In J. A. Schinka & W. F. Velicer, (Eds.), Handbook of psychology Volume 2: Research methods in Psychology (pp. 33-64). New Jersey: John Wiley & Sons, Inc. Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2, 131-160. Berk, R. A. (2008). Statistical learning from a regression perspective. New York: Springer. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Monterey, CA: Wadsworth International Group. Carpio, K.J.E. & Hermosilla, A.Y. (2002), On multicollinearity and artificial neural networks, Complexity International, 10, Retrieved October 8, 2009, from http://www.complexity.org.au/ci/vol10/hermos01/.
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publishDate 2010-06-30
date_accessioned 2010-06-30T00:00:00Z
date_available 2010-06-30T00:00:00Z
url https://revistas.usb.edu.co/index.php/IJPR/article/view/819
url_doi https://doi.org/10.21500/20112084.819
issn 2011-2084
eissn 2011-7922
doi 10.21500/20112084.819
citationstartpage 9
citationendpage 22
url2_str_mv https://revistas.usb.edu.co/index.php/IJPR/article/download/819/595
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