Improvements for determining the number of clusters in k-means for innovation databases in SMEs

The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this stu...

Full description

Autores:
amelec, viloria
Pineda Lezama, Omar Bonerge
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4834
Acceso en línea:
https://hdl.handle.net/11323/4834
https://repositorio.cuc.edu.co/
Palabra clave:
k-means
automatic clustering
differential evolution
k activation threshold
U-Control Chart
SMEs
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_3c764af7458a676038b6edbd740cf337
oai_identifier_str oai:repositorio.cuc.edu.co:11323/4834
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Improvements for determining the number of clusters in k-means for innovation databases in SMEs
title Improvements for determining the number of clusters in k-means for innovation databases in SMEs
spellingShingle Improvements for determining the number of clusters in k-means for innovation databases in SMEs
k-means
automatic clustering
differential evolution
k activation threshold
U-Control Chart
SMEs
title_short Improvements for determining the number of clusters in k-means for innovation databases in SMEs
title_full Improvements for determining the number of clusters in k-means for innovation databases in SMEs
title_fullStr Improvements for determining the number of clusters in k-means for innovation databases in SMEs
title_full_unstemmed Improvements for determining the number of clusters in k-means for innovation databases in SMEs
title_sort Improvements for determining the number of clusters in k-means for innovation databases in SMEs
dc.creator.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
dc.subject.spa.fl_str_mv k-means
automatic clustering
differential evolution
k activation threshold
U-Control Chart
SMEs
topic k-means
automatic clustering
differential evolution
k activation threshold
U-Control Chart
SMEs
description The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this study, the problem of ACDE is enhanced using the U Control Chart (UCC). The performance of the proposed method was tested using five data sets from the National Administrative Department of Statistics (DANE - Departamento Administrativo Nacional de Estadísticas) and the Ministry of Commerce, Industry, and Tourism of Colombia for the innovative capacity of Small and Medium-sized Enterprises (SMEs) and were assessed by the Davies Bouldin Index (DBI) and the Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T13:50:20Z
dc.date.available.none.fl_str_mv 2019-06-10T13:50:20Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 0000-2010
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/4834
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0000-2010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/4834
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408. [2] Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [3] Bartels, F.; Koria, R. 2014. Mapping, measuring and managing African national systems of innovation for policy and development: the case of the Ghana national system of innovation. African J. Science, Technol., Innov. Developm. 6(5):383-400. [4] DANE. 2017. Documento metodológico encuesta de desarrollo e innovación tecnológica en la industria Manufacturera. Bogotá: DANE. 43p. [5] Jolliffe, I. 2002. Principal component analysis. Hoboken: John Wiley & Sons, 488p. [6] Chakraborty, S., Das, S., 2018. Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148– 156. https://doi.org/10.1016/j.spl.2018.01.015 [7] Garcia, A.J., Flores, W.G., 2016. Automatic Clustering Using Nature-Inspired Metaheuristics: A Survey. Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2015.12.001 [8] Das, S., Abraham, A., Konar, A., 2008. Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans 38, 218–237. https://doi.org/10.1109/TSMCA.2007.909595 [9] Ramadas, M., Abraham, A., Kumar, S., 2016. FSDE-Forced Strategy Differential Evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2016.12.005. [10] Kuo, R.., Suryani Erma, Yasid, A., 2013. Automatic Clustering Combining Differential Evolution Algorithm and k-Means Algorithm. Proc. Inst. Ind. Eng. Asian Conf. 2013 1207–1215. https://doi.org/10.1007/978-981-4451-98-7 [11] Kaya, I., 2009. A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny). 179, 1552–1566. https://doi.org/10.1016/j.ins.2008.09.024 [12] Tam, H., Ng, S., Lui, A.K., Leung, M., 2017. Improved Activation Schema on Automatic Clustering Using Differential Evolution Algorithm. IEEE Congr. Evol. Comput. 1749–1756. https://doi.org/10.1109/CEC.2017.7969513 [13] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018) Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [14] Varela Izquierdo N., Cabrera H.R., Lopez Carvajal G., Viloria A., Gaitán Angulo M., Henry MA. (2018) Methodology for the Reduction and Integration of Data in the Performance Measurement of Industries Cement Plants. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [15] Gaitán-Angulo M. Jairo Enrique Santander Abril, Amelec Viloria, Julio Mojica Herazo, Pedro Hernández Malpica, Jairo Luis Martínez Ventura, Lissette Hernández-Fernández. (2018). Company Family, Innovation and Colombian Graphic Industry: A Bayesian Estimation of a Logistical Model. In: T
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Procedia Computer Science
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/ba95a06b-d750-4d9e-8aa5-346e4e6a2b68/download
https://repositorio.cuc.edu.co/bitstreams/211e211e-46d9-4880-bc1a-b61533bddc75/download
https://repositorio.cuc.edu.co/bitstreams/c108a77d-5e2a-4bc6-9ba3-5b7dae20ca81/download
https://repositorio.cuc.edu.co/bitstreams/7155b4b1-111c-4c8e-b11e-f2e8833b110e/download
https://repositorio.cuc.edu.co/bitstreams/99b0856d-c6d6-483c-ac3a-45c83fed3bc2/download
bitstream.checksum.fl_str_mv cc71e90410b1bbc8b5c9f3703400ea22
4460e5956bc1d1639be9ae6146a50347
8a4605be74aa9ea9d79846c1fba20a33
f7ecebd5ab848d06f6c8cc74d01f2058
89f07be103cc954d110aaedcf4c21714
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166841975963648
spelling amelec, viloriaPineda Lezama, Omar Bonerge2019-06-10T13:50:20Z2019-06-10T13:50:20Z20190000-2010https://hdl.handle.net/11323/4834Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this study, the problem of ACDE is enhanced using the U Control Chart (UCC). The performance of the proposed method was tested using five data sets from the National Administrative Department of Statistics (DANE - Departamento Administrativo Nacional de Estadísticas) and the Ministry of Commerce, Industry, and Tourism of Colombia for the innovative capacity of Small and Medium-sized Enterprises (SMEs) and were assessed by the Davies Bouldin Index (DBI) and the Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering.amelec, viloria-6305b089-116f-431f-9ab3-52b11c6194dd-600Pineda Lezama, Omar Bonerge-365a03a0-145e-4df5-9abe-f5ccf9d96612-0engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2k-meansautomatic clusteringdifferential evolutionk activation thresholdU-Control ChartSMEsImprovements for determining the number of clusters in k-means for innovation databases in SMEsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Amelec, V. (2015). Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Advanced Science Letters, 21(5), 1406-1408. [2] Lis-Gutiérrez M., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [3] Bartels, F.; Koria, R. 2014. Mapping, measuring and managing African national systems of innovation for policy and development: the case of the Ghana national system of innovation. African J. Science, Technol., Innov. Developm. 6(5):383-400. [4] DANE. 2017. Documento metodológico encuesta de desarrollo e innovación tecnológica en la industria Manufacturera. Bogotá: DANE. 43p. [5] Jolliffe, I. 2002. Principal component analysis. Hoboken: John Wiley & Sons, 488p. [6] Chakraborty, S., Das, S., 2018. Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148– 156. https://doi.org/10.1016/j.spl.2018.01.015 [7] Garcia, A.J., Flores, W.G., 2016. Automatic Clustering Using Nature-Inspired Metaheuristics: A Survey. Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2015.12.001 [8] Das, S., Abraham, A., Konar, A., 2008. Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans 38, 218–237. https://doi.org/10.1109/TSMCA.2007.909595 [9] Ramadas, M., Abraham, A., Kumar, S., 2016. FSDE-Forced Strategy Differential Evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2016.12.005. [10] Kuo, R.., Suryani Erma, Yasid, A., 2013. Automatic Clustering Combining Differential Evolution Algorithm and k-Means Algorithm. Proc. Inst. Ind. Eng. Asian Conf. 2013 1207–1215. https://doi.org/10.1007/978-981-4451-98-7 [11] Kaya, I., 2009. A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny). 179, 1552–1566. https://doi.org/10.1016/j.ins.2008.09.024 [12] Tam, H., Ng, S., Lui, A.K., Leung, M., 2017. Improved Activation Schema on Automatic Clustering Using Differential Evolution Algorithm. IEEE Congr. Evol. Comput. 1749–1756. https://doi.org/10.1109/CEC.2017.7969513 [13] Kamatkar S.J., Tayade A., Viloria A., Hernández-Chacín A. (2018) Application of Classification Technique of Data Mining for Employee Management System. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [14] Varela Izquierdo N., Cabrera H.R., Lopez Carvajal G., Viloria A., Gaitán Angulo M., Henry MA. (2018) Methodology for the Reduction and Integration of Data in the Performance Measurement of Industries Cement Plants. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham. [15] Gaitán-Angulo M. Jairo Enrique Santander Abril, Amelec Viloria, Julio Mojica Herazo, Pedro Hernández Malpica, Jairo Luis Martínez Ventura, Lissette Hernández-Fernández. (2018). Company Family, Innovation and Colombian Graphic Industry: A Bayesian Estimation of a Logistical Model. In: TPublicationORIGINALImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdfImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdfapplication/pdf457383https://repositorio.cuc.edu.co/bitstreams/ba95a06b-d750-4d9e-8aa5-346e4e6a2b68/downloadcc71e90410b1bbc8b5c9f3703400ea22MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/211e211e-46d9-4880-bc1a-b61533bddc75/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/c108a77d-5e2a-4bc6-9ba3-5b7dae20ca81/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdf.jpgImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdf.jpgimage/jpeg44211https://repositorio.cuc.edu.co/bitstreams/7155b4b1-111c-4c8e-b11e-f2e8833b110e/downloadf7ecebd5ab848d06f6c8cc74d01f2058MD55TEXTImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdf.txtImprovements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs.pdf.txttext/plain27253https://repositorio.cuc.edu.co/bitstreams/99b0856d-c6d6-483c-ac3a-45c83fed3bc2/download89f07be103cc954d110aaedcf4c21714MD5611323/4834oai:repositorio.cuc.edu.co:11323/48342024-09-17 14:16:58.285http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares