Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets

Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for...
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Autor*in:	Jeonghwan Gwak [verfasserIn] Harish Garg [verfasserIn] Naeem Jan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Clustering analysis Soft set Intuitionistic fuzzy soft set Bipolar intuitionistic fuzzy soft set Bipolar complex intuitionistic fuzzy soft set

Übergeordnetes Werk:	In: Alexandria Engineering Journal - Elsevier, 2016, 67(2023), Seite 473-487
Übergeordnetes Werk:	volume:67 ; year:2023 ; pages:473-487

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.aej.2022.12.003

Katalog-ID:	DOAJ082735956

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allfieldsGer	10.1016/j.aej.2022.12.003 doi (DE-627)DOAJ082735956 (DE-599)DOAJ3d5daf69147c49919090aeb80547232e DE-627 ger DE-627 rakwb eng TA1-2040 Jeonghwan Gwak verfasserin aut Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs (“bipolar complex intuitionistic fuzzy soft sets”) by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given. Clustering analysis Soft set Intuitionistic fuzzy soft set Bipolar intuitionistic fuzzy soft set Bipolar complex intuitionistic fuzzy soft set Engineering (General). Civil engineering (General) Harish Garg verfasserin aut Naeem Jan verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 67(2023), Seite 473-487 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:67 year:2023 pages:473-487 https://doi.org/10.1016/j.aej.2022.12.003 kostenfrei https://doaj.org/article/3d5daf69147c49919090aeb80547232e kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016822007888 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 67 2023 473-487
allfieldsSound	10.1016/j.aej.2022.12.003 doi (DE-627)DOAJ082735956 (DE-599)DOAJ3d5daf69147c49919090aeb80547232e DE-627 ger DE-627 rakwb eng TA1-2040 Jeonghwan Gwak verfasserin aut Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs (“bipolar complex intuitionistic fuzzy soft sets”) by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given. Clustering analysis Soft set Intuitionistic fuzzy soft set Bipolar intuitionistic fuzzy soft set Bipolar complex intuitionistic fuzzy soft set Engineering (General). Civil engineering (General) Harish Garg verfasserin aut Naeem Jan verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 67(2023), Seite 473-487 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:67 year:2023 pages:473-487 https://doi.org/10.1016/j.aej.2022.12.003 kostenfrei https://doaj.org/article/3d5daf69147c49919090aeb80547232e kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016822007888 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 67 2023 473-487
language	English
source	In Alexandria Engineering Journal 67(2023), Seite 473-487 volume:67 year:2023 pages:473-487
sourceStr	In Alexandria Engineering Journal 67(2023), Seite 473-487 volume:67 year:2023 pages:473-487
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Clustering analysis Soft set Intuitionistic fuzzy soft set Bipolar intuitionistic fuzzy soft set Bipolar complex intuitionistic fuzzy soft set Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	Alexandria Engineering Journal
authorswithroles_txt_mv	Jeonghwan Gwak @@aut@@ Harish Garg @@aut@@ Naeem Jan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	669887609
id	DOAJ082735956
language_de	englisch
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callnumber-first	T - Technology
author	Jeonghwan Gwak
spellingShingle	Jeonghwan Gwak misc TA1-2040 misc Clustering analysis misc Soft set misc Intuitionistic fuzzy soft set misc Bipolar intuitionistic fuzzy soft set misc Bipolar complex intuitionistic fuzzy soft set misc Engineering (General). Civil engineering (General) Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
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topic_title	TA1-2040 Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets Clustering analysis Soft set Intuitionistic fuzzy soft set Bipolar intuitionistic fuzzy soft set Bipolar complex intuitionistic fuzzy soft set
topic	misc TA1-2040 misc Clustering analysis misc Soft set misc Intuitionistic fuzzy soft set misc Bipolar intuitionistic fuzzy soft set misc Bipolar complex intuitionistic fuzzy soft set misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc Clustering analysis misc Soft set misc Intuitionistic fuzzy soft set misc Bipolar intuitionistic fuzzy soft set misc Bipolar complex intuitionistic fuzzy soft set misc Engineering (General). Civil engineering (General)
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title	Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
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title_full	Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
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title_sort	hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
callnumber	TA1-2040
title_auth	Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
abstract	Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs (“bipolar complex intuitionistic fuzzy soft sets”) by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given.
abstractGer	Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs (“bipolar complex intuitionistic fuzzy soft sets”) by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given.
abstract_unstemmed	Data clustering is an instrumental tool in the area of energy management resources, marketing, and business. Clustering helps to increase productivity, facilitate decision-making, and generate new business opportunities. On the other hand, the soft set theory provides a general mathematical tool for dealing with uncertain, and vague information. In this paper, we present the novel concept of the BCIFSSs (“bipolar complex intuitionistic fuzzy soft sets”) by merging bipolar complex intuitionistic fuzzy sets and soft sets. Also, we explain their basic operations including complement, union, and intersection with some appropriate examples. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems for handling ambiguity. Further, the BCIFSSs have an extensive structure because it discusses both grades of memberships (Mem-S) and non-memberships (Non-Mem-S) with positive and negative aspects and can also deal with multivariable difficulties. Later on, we stated a decision-making algorithm and real-world examples to demonstrate the effectiveness, and applicability of the proposed concept. The BCIFSSs show the dual grades of both the Mem-S and Non-Mem-S in the decision-making process. Finally, the comparative analysis of introduced frameworks with some pre-existing ideas is given.
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title_short	Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets
url	https://doi.org/10.1016/j.aej.2022.12.003 https://doaj.org/article/3d5daf69147c49919090aeb80547232e http://www.sciencedirect.com/science/article/pii/S1110016822007888 https://doaj.org/toc/1110-0168
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Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets

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