Hierarchical clustering of interval-valued intuitionistic fuzzy relations and its application to elicit criteria weights in MCDM problems

Mamata Sahu (), Anjana Gupta () and Aparna Mehra ()
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Mamata Sahu: Delhi Technological University
Anjana Gupta: Delhi Technological University
Aparna Mehra: Indian Institute of Technology Delhi

OPSEARCH, 2017, vol. 54, issue 2, No 9, 388-416

Abstract: Abstract The paper aims to apply the $$({\widetilde{\alpha }}, {\widetilde{\beta }})$$ ( α ~ , β ~ ) -cuts and the resolution form of the interval-valued intuitionistic fuzzy (IVIF ) relations to develop a procedure for constructing a hierarchical clustering for IVIF max–min similarity relations. The advantage of the proposed scheme is illustrated in determining the criteria weights in multi-criteria decision making (MCDM) problems involving IVIF numbers. The problem of finding the criteria weights is of critical interest in the domain of MCDM problems . A complete procedure is drawn to generate criteria weights starting from the criteria-alternative matrix of the MCDM problem with entries provided by a decision maker as IVIF numbers .

Keywords: Interval-valued intuitionistic fuzzy set; Interval-valued intuitionistic fuzzy similarity relation; Hierarchical clustering; Max–min composition; Multi-criteria decision making problem (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12597-016-0282-5

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