Dimensionality reduction - A soft set-theoretic and soft graph approach

Due to the digitization of information, organizations have abundant data in databases. Large-scale data are equally important and complex hence gathering, storing, understanding, and analyzing data is a problem for organizations. To extract information from this superfluous data, the need for dime...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Omdutt Sharma, Pratiksha Tiwari, Priti Gupta
Formato:	article
Lenguaje:	EN
Publicado:	Prince of Songkla University 2021
Materias:	dimensionality reduction soft set grade membership binary-valued information system soft graph Technology T Technology (General) T1-995 Science Q Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/eafde47fed1b4298acc24009ca212fc4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:eafde47fed1b4298acc24009ca212fc4
record_format	dspace
spelling	oai:doaj.org-article:eafde47fed1b4298acc24009ca212fc42021-11-07T16:44:10ZDimensionality reduction - A soft set-theoretic and soft graph approach10.14456/sjst-psu.2021.1400125-3395https://doaj.org/article/eafde47fed1b4298acc24009ca212fc42021-08-01T00:00:00Zhttps://rdo.psu.ac.th/sjstweb/journal/43-4/20.pdfhttps://doaj.org/toc/0125-3395Due to the digitization of information, organizations have abundant data in databases. Large-scale data are equally important and complex hence gathering, storing, understanding, and analyzing data is a problem for organizations. To extract information from this superfluous data, the need for dimensionality reduction increases. Soft set theory has been efficaciously applied and solved problems of dimensionality, which saves the cost of computation, reduces noise, and redundancy in data. Different methods and measures are developed by researchers for the reduction of dimensions, in which some are probabilistic, and some are non-probabilistic. In this paper, a non-probabilistic approach is developed by using soft set theory for dimensionality reduction. Further, an algorithm of dimensionality reduction through bipartite graphs is also described. Lastly, the proposed algorithm is applied to a case study, and a comparison of results indicates that the proposed algorithm is better than the existing algorithms. Omdutt SharmaPratiksha TiwariPriti GuptaPrince of Songkla Universityarticledimensionality reductionsoft setgrade membershipbinary-valued information systemsoft graphTechnologyTTechnology (General)T1-995ScienceQScience (General)Q1-390ENSongklanakarin Journal of Science and Technology (SJST), Vol 43, Iss 4, Pp 1063-1070 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dimensionality reduction soft set grade membership binary-valued information system soft graph Technology T Technology (General) T1-995 Science Q Science (General) Q1-390
spellingShingle	dimensionality reduction soft set grade membership binary-valued information system soft graph Technology T Technology (General) T1-995 Science Q Science (General) Q1-390 Omdutt Sharma Pratiksha Tiwari Priti Gupta Dimensionality reduction - A soft set-theoretic and soft graph approach
description	Due to the digitization of information, organizations have abundant data in databases. Large-scale data are equally important and complex hence gathering, storing, understanding, and analyzing data is a problem for organizations. To extract information from this superfluous data, the need for dimensionality reduction increases. Soft set theory has been efficaciously applied and solved problems of dimensionality, which saves the cost of computation, reduces noise, and redundancy in data. Different methods and measures are developed by researchers for the reduction of dimensions, in which some are probabilistic, and some are non-probabilistic. In this paper, a non-probabilistic approach is developed by using soft set theory for dimensionality reduction. Further, an algorithm of dimensionality reduction through bipartite graphs is also described. Lastly, the proposed algorithm is applied to a case study, and a comparison of results indicates that the proposed algorithm is better than the existing algorithms.
format	article
author	Omdutt Sharma Pratiksha Tiwari Priti Gupta
author_facet	Omdutt Sharma Pratiksha Tiwari Priti Gupta
author_sort	Omdutt Sharma
title	Dimensionality reduction - A soft set-theoretic and soft graph approach
title_short	Dimensionality reduction - A soft set-theoretic and soft graph approach
title_full	Dimensionality reduction - A soft set-theoretic and soft graph approach
title_fullStr	Dimensionality reduction - A soft set-theoretic and soft graph approach
title_full_unstemmed	Dimensionality reduction - A soft set-theoretic and soft graph approach
title_sort	dimensionality reduction - a soft set-theoretic and soft graph approach
publisher	Prince of Songkla University
publishDate	2021
url	https://doaj.org/article/eafde47fed1b4298acc24009ca212fc4
work_keys_str_mv	AT omduttsharma dimensionalityreductionasoftsettheoreticandsoftgraphapproach AT pratikshatiwari dimensionalityreductionasoftsettheoreticandsoftgraphapproach AT pritigupta dimensionalityreductionasoftsettheoreticandsoftgraphapproach
_version_	1718443323503411200

Dimensionality reduction - A soft set-theoretic and soft graph approach

Ejemplares similares