Parallelization of the Apriori Algorithm for the Search of Frequent Elements

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There is a wide variety of techniques that increase application performance by alleviating one or more of the most important problems with today's processors. In this work, the execution time, speedup and efficiency of the linear Apriori algorithm are shown as well as parallel with the use of O...

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Detalles Bibliográficos
Autores: Mamani Coaquira, Yonatan, Chumpisuca Carrion, Edith
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Nacional Micaela Bastidas de Apurímac
Repositorio:UNAMBA-Institucional
Lenguaje:español
OAI Identifier:oai:172.16.0.151:UNAMBA/952
Enlace del recurso:http://repositorio.unamba.edu.pe/handle/UNAMBA/952
Nivel de acceso:acceso abierto
Materia:Apriori algorithm
Frequent itemsets
Parallel algorithm
Openmp
https://purl.org/pe-repo/ocde/ford#1.02.01
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dc.title.es_PE.fl_str_mv Parallelization of the Apriori Algorithm for the Search of Frequent Elements
title Parallelization of the Apriori Algorithm for the Search of Frequent Elements
spellingShingle Parallelization of the Apriori Algorithm for the Search of Frequent Elements
Mamani Coaquira, Yonatan
Apriori algorithm
Frequent itemsets
Parallel algorithm
Openmp
https://purl.org/pe-repo/ocde/ford#1.02.01
title_short Parallelization of the Apriori Algorithm for the Search of Frequent Elements
title_full Parallelization of the Apriori Algorithm for the Search of Frequent Elements
title_fullStr Parallelization of the Apriori Algorithm for the Search of Frequent Elements
title_full_unstemmed Parallelization of the Apriori Algorithm for the Search of Frequent Elements
title_sort Parallelization of the Apriori Algorithm for the Search of Frequent Elements
author Mamani Coaquira, Yonatan
author_facet Mamani Coaquira, Yonatan
Chumpisuca Carrion, Edith
author_role author
author2 Chumpisuca Carrion, Edith
author2_role author
dc.contributor.author.fl_str_mv Mamani Coaquira, Yonatan
Chumpisuca Carrion, Edith
dc.subject.es_PE.fl_str_mv Apriori algorithm
Frequent itemsets
topic Apriori algorithm
Frequent itemsets
Parallel algorithm
Openmp
https://purl.org/pe-repo/ocde/ford#1.02.01
dc.subject.none.fl_str_mv Parallel algorithm
Openmp
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.01
description There is a wide variety of techniques that increase application performance by alleviating one or more of the most important problems with today's processors. In this work, the execution time, speedup and efficiency of the linear Apriori algorithm are shown as well as parallel with the use of OpenMP. By identifying the frequent elements of transactional databases, in processing 5 thousand records the time improves in 42,078 seconds of the algorithm with openMP compared to the sequential algorithm, in the execution 8 processor cores were used.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2021-05-24T00:45:14Z
dc.date.available.none.fl_str_mv 2021-05-24T00:45:14Z
dc.date.issued.fl_str_mv 2020-03-20
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.es_PE.fl_str_mv IEEE
dc.identifier.issn.none.fl_str_mv 2709-8990
dc.identifier.uri.none.fl_str_mv http://repositorio.unamba.edu.pe/handle/UNAMBA/952
dc.identifier.journal.es_PE.fl_str_mv Revista de Investigación Micaela
identifier_str_mv IEEE
2709-8990
Revista de Investigación Micaela
url http://repositorio.unamba.edu.pe/handle/UNAMBA/952
dc.language.iso.es_PE.fl_str_mv spa
language spa
dc.relation.es_PE.fl_str_mv info:pe-repo/semantics/software
dc.relation.ispartofseries.none.fl_str_mv Volumen 01;2020
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/us/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Universidad Nacional Micaela Bastidas de Apurímac
dc.source.es_PE.fl_str_mv Universidad Nacional Micaela Bastidas de Apurímac
Repositorio Institucional - UNAMBA
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instname:Universidad Nacional Micaela Bastidas de Apurímac
instacron:UNAMBA
instname_str Universidad Nacional Micaela Bastidas de Apurímac
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institution UNAMBA
reponame_str UNAMBA-Institucional
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spelling Mamani Coaquira, YonatanChumpisuca Carrion, Edith2021-05-24T00:45:14Z2021-05-24T00:45:14Z2020-03-20IEEE2709-8990http://repositorio.unamba.edu.pe/handle/UNAMBA/952Revista de Investigación MicaelaThere is a wide variety of techniques that increase application performance by alleviating one or more of the most important problems with today's processors. In this work, the execution time, speedup and efficiency of the linear Apriori algorithm are shown as well as parallel with the use of OpenMP. By identifying the frequent elements of transactional databases, in processing 5 thousand records the time improves in 42,078 seconds of the algorithm with openMP compared to the sequential algorithm, in the execution 8 processor cores were used.Submitted by Ecler Mamani (eclervirtual@gmail.com) on 2021-05-24T00:45:14Z No. of bitstreams: 2 license_rdf: 1536 bytes, checksum: df76b173e7954a20718100d078b240a8 (MD5) 23-27 M2020.pdf: 227224 bytes, checksum: baf242cd044a26217469b59f958827c1 (MD5)Made available in DSpace on 2021-05-24T00:45:14Z (GMT). No. of bitstreams: 2 license_rdf: 1536 bytes, checksum: df76b173e7954a20718100d078b240a8 (MD5) 23-27 M2020.pdf: 227224 bytes, checksum: baf242cd044a26217469b59f958827c1 (MD5) Previous issue date: 2020-03-20Paresapplication/pdfspaUniversidad Nacional Micaela Bastidas de Apurímacinfo:pe-repo/semantics/softwareVolumen 01;2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/us/Universidad Nacional Micaela Bastidas de ApurímacRepositorio Institucional - UNAMBAreponame:UNAMBA-Institucionalinstname:Universidad Nacional Micaela Bastidas de Apurímacinstacron:UNAMBAApriori algorithmFrequent itemsetsParallel algorithmOpenmphttps://purl.org/pe-repo/ocde/ford#1.02.01Parallelization of the Apriori Algorithm for the Search of Frequent Elementsinfo:eu-repo/semantics/articleTEXT23-27 M2020.pdf.txt23-27 M2020.pdf.txtExtracted texttext/plain18777http://172.16.0.151/bitstream/UNAMBA/952/4/23-27%20M2020.pdf.txt88b59d702a59192ddb593aec14192be3MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81327http://172.16.0.151/bitstream/UNAMBA/952/3/license.txtc52066b9c50a8f86be96c82978636682MD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81536http://172.16.0.151/bitstream/UNAMBA/952/2/license_rdfdf76b173e7954a20718100d078b240a8MD52ORIGINAL23-27 M2020.pdf23-27 M2020.pdfTexto completoapplication/pdf227224http://172.16.0.151/bitstream/UNAMBA/952/1/23-27%20M2020.pdfbaf242cd044a26217469b59f958827c1MD51UNAMBA/952oai:172.16.0.151:UNAMBA/9522024-10-17 16:07:27.276DSpaceathos2777@gmail.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
score 13.95948
Parallelization of the Apriori Algorithm for the Search of Frequent Elements
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