An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/58653 |
Resumo: | Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this work |
id |
UEM-6_e9a5a98dd3937e679f925e3de2694bd0 |
---|---|
oai_identifier_str |
oai:periodicos.uem.br/ojs:article/58653 |
network_acronym_str |
UEM-6 |
network_name_str |
Acta scientiarum. Technology (Online) |
repository_id_str |
|
spelling |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media Collaborative filtering; clustering algorithm; data mining; recommender systems; social mediaCollaborative filtering; clustering algorithm; data mining; recommender systems; social mediaSocial media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this workSocial media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this workUniversidade Estadual De Maringá2022-03-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/5865310.4025/actascitechnol.v44i1.58653Acta Scientiarum. Technology; Vol 44 (2022): Publicação contínua; e58653Acta Scientiarum. Technology; v. 44 (2022): Publicação contínua; e586531806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/58653/751375153853Copyright (c) 2022 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRenjith, ShiniSreekumar , A.Jathavedan , M.2022-04-01T17:54:58Zoai:periodicos.uem.br/ojs:article/58653Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2022-04-01T17:54:58Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
title |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
spellingShingle |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media Renjith, Shini Collaborative filtering; clustering algorithm; data mining; recommender systems; social media Collaborative filtering; clustering algorithm; data mining; recommender systems; social media |
title_short |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
title_full |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
title_fullStr |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
title_full_unstemmed |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
title_sort |
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media |
author |
Renjith, Shini |
author_facet |
Renjith, Shini Sreekumar , A. Jathavedan , M. |
author_role |
author |
author2 |
Sreekumar , A. Jathavedan , M. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Renjith, Shini Sreekumar , A. Jathavedan , M. |
dc.subject.por.fl_str_mv |
Collaborative filtering; clustering algorithm; data mining; recommender systems; social media Collaborative filtering; clustering algorithm; data mining; recommender systems; social media |
topic |
Collaborative filtering; clustering algorithm; data mining; recommender systems; social media Collaborative filtering; clustering algorithm; data mining; recommender systems; social media |
description |
Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this work |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-11 |
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 |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/58653 10.4025/actascitechnol.v44i1.58653 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/58653 |
identifier_str_mv |
10.4025/actascitechnol.v44i1.58653 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/58653/751375153853 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 44 (2022): Publicação contínua; e58653 Acta Scientiarum. Technology; v. 44 (2022): Publicação contínua; e58653 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315337963896832 |