Clustering of Quantitative Survey Data based on Marking Patterns

Detalhes bibliográficos
Autor(a) principal: Sadh, Roopam
Data de Publicação: 2020
Outros Autores: Kumar, Rajeev
Tipo de documento: Artigo
Idioma: eng
Título da fonte: INFOCOMP: Jornal de Ciência da Computação
Texto Completo: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009
Resumo: Clustering of quantitative survey data is done in-order to identify the divergent and dominant behaviors of the respondents. It is intended to explore the general tendencies of the respondent groups. Popular clustering methods working on value based similarity are inappropriate for survey data due to its distinct properties. Since marking patterns in survey data represents respondent’s behavior, hence separating the responses on the basis of marking patterns is an effective approach to identify the dominant behaviors. Thus, in this paper, we propose a specialized clustering method for quantitative survey data that combines the features of both, value based as well as pattern based approaches in order to obtain meaningful results. The proposed method does not require presetting of the clustering parameters while it makes use of group labels for selecting features and guiding the centroids at positions, which best describe divergent marking habits. We apply the proposed method over an educational survey dataset and compare its results with K-means clustering with respect to the benchmark stakeholder theory. Comparison results show that the proposed method is more appropriate for quantitative survey data.
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spelling Clustering of Quantitative Survey Data based on Marking PatternsClustering of quantitative survey data is done in-order to identify the divergent and dominant behaviors of the respondents. It is intended to explore the general tendencies of the respondent groups. Popular clustering methods working on value based similarity are inappropriate for survey data due to its distinct properties. Since marking patterns in survey data represents respondent’s behavior, hence separating the responses on the basis of marking patterns is an effective approach to identify the dominant behaviors. Thus, in this paper, we propose a specialized clustering method for quantitative survey data that combines the features of both, value based as well as pattern based approaches in order to obtain meaningful results. The proposed method does not require presetting of the clustering parameters while it makes use of group labels for selecting features and guiding the centroids at positions, which best describe divergent marking habits. We apply the proposed method over an educational survey dataset and compare its results with K-means clustering with respect to the benchmark stakeholder theory. Comparison results show that the proposed method is more appropriate for quantitative survey data.Editora da UFLA2020-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009INFOCOMP Journal of Computer Science; Vol. 19 No. 2 (2020): December 2020; 109-1191982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009/547Copyright (c) 2020 Roopam Sadh, Rajeev Kumarinfo:eu-repo/semantics/openAccessSadh, RoopamKumar, Rajeev2020-12-01T21:34:08Zoai:infocomp.dcc.ufla.br:article/1009Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:45.896816INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Clustering of Quantitative Survey Data based on Marking Patterns
title Clustering of Quantitative Survey Data based on Marking Patterns
spellingShingle Clustering of Quantitative Survey Data based on Marking Patterns
Sadh, Roopam
title_short Clustering of Quantitative Survey Data based on Marking Patterns
title_full Clustering of Quantitative Survey Data based on Marking Patterns
title_fullStr Clustering of Quantitative Survey Data based on Marking Patterns
title_full_unstemmed Clustering of Quantitative Survey Data based on Marking Patterns
title_sort Clustering of Quantitative Survey Data based on Marking Patterns
author Sadh, Roopam
author_facet Sadh, Roopam
Kumar, Rajeev
author_role author
author2 Kumar, Rajeev
author2_role author
dc.contributor.author.fl_str_mv Sadh, Roopam
Kumar, Rajeev
description Clustering of quantitative survey data is done in-order to identify the divergent and dominant behaviors of the respondents. It is intended to explore the general tendencies of the respondent groups. Popular clustering methods working on value based similarity are inappropriate for survey data due to its distinct properties. Since marking patterns in survey data represents respondent’s behavior, hence separating the responses on the basis of marking patterns is an effective approach to identify the dominant behaviors. Thus, in this paper, we propose a specialized clustering method for quantitative survey data that combines the features of both, value based as well as pattern based approaches in order to obtain meaningful results. The proposed method does not require presetting of the clustering parameters while it makes use of group labels for selecting features and guiding the centroids at positions, which best describe divergent marking habits. We apply the proposed method over an educational survey dataset and compare its results with K-means clustering with respect to the benchmark stakeholder theory. Comparison results show that the proposed method is more appropriate for quantitative survey data.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009
url https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1009/547
dc.rights.driver.fl_str_mv Copyright (c) 2020 Roopam Sadh, Rajeev Kumar
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 Roopam Sadh, Rajeev Kumar
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editora da UFLA
publisher.none.fl_str_mv Editora da UFLA
dc.source.none.fl_str_mv INFOCOMP Journal of Computer Science; Vol. 19 No. 2 (2020): December 2020; 109-119
1982-3363
1807-4545
reponame:INFOCOMP: Jornal de Ciência da Computação
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str INFOCOMP: Jornal de Ciência da Computação
collection INFOCOMP: Jornal de Ciência da Computação
repository.name.fl_str_mv INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv infocomp@dcc.ufla.br||apfreire@dcc.ufla.br
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