A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data

Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a...

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Autores principales:	Jhila Nasiri, Farzin Modarres Khiyabani, NIma Azorbaarmir Shotorbani
Formato:	article
Lenguaje:	FA
Publicado:	Islamic Azad University, Tabriz Branch 2021
Materias:	clustering data mining productivity swarm intelligence Management. Industrial management HD28-70
Acceso en línea:	https://doaj.org/article/61de69f0ff2c4046943252842f1ea39f
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id	oai:doaj.org-article:61de69f0ff2c4046943252842f1ea39f
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spelling	oai:doaj.org-article:61de69f0ff2c4046943252842f1ea39f2021-11-09T05:40:12ZA New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data2716-99792476-729810.30495/qjopm.2020.1867405.2443https://doaj.org/article/61de69f0ff2c4046943252842f1ea39f2021-09-01T00:00:00Zhttp://jpm.iaut.ac.ir/article_684606_c7bd27edf2632b860290bb4371869950.pdfhttps://doaj.org/toc/2716-9979https://doaj.org/toc/2476-7298Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.Jhila NasiriFarzin Modarres KhiyabaniNIma Azorbaarmir ShotorbaniIslamic Azad University, Tabriz Brancharticleclusteringdata miningproductivityswarm intelligenceManagement. Industrial managementHD28-70FAمدیریت بهره وری, Vol 15, Iss 3(58)پاییز, Pp 145-161 (2021)
institution	DOAJ
collection	DOAJ
language	FA
topic	clustering data mining productivity swarm intelligence Management. Industrial management HD28-70
spellingShingle	clustering data mining productivity swarm intelligence Management. Industrial management HD28-70 Jhila Nasiri Farzin Modarres Khiyabani NIma Azorbaarmir Shotorbani A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
description	Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.
format	article
author	Jhila Nasiri Farzin Modarres Khiyabani NIma Azorbaarmir Shotorbani
author_facet	Jhila Nasiri Farzin Modarres Khiyabani NIma Azorbaarmir Shotorbani
author_sort	Jhila Nasiri
title	A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
title_short	A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
title_full	A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
title_fullStr	A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
title_full_unstemmed	A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
title_sort	new clustering algorithm for productivity in data mining: the case of uca data
publisher	Islamic Azad University, Tabriz Branch
publishDate	2021
url	https://doaj.org/article/61de69f0ff2c4046943252842f1ea39f
work_keys_str_mv	AT jhilanasiri anewclusteringalgorithmforproductivityindataminingthecaseofucadata AT farzinmodarreskhiyabani anewclusteringalgorithmforproductivityindataminingthecaseofucadata AT nimaazorbaarmirshotorbani anewclusteringalgorithmforproductivityindataminingthecaseofucadata AT jhilanasiri newclusteringalgorithmforproductivityindataminingthecaseofucadata AT farzinmodarreskhiyabani newclusteringalgorithmforproductivityindataminingthecaseofucadata AT nimaazorbaarmirshotorbani newclusteringalgorithmforproductivityindataminingthecaseofucadata
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A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data

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