Multi-criteria classification, sorting, ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda
Author(s) :
Amor, Sarah [Auteur]
Belaid, Fateh [Auteur]
Lille économie management - UMR 9221 [LEM]
Benkraiem, Ramzi [Auteur]
Audencia Business School
Ramdani, Boumediene [Auteur]
Qatar University
Guesmi, Khaled [Auteur]
EDC Paris Business School [EDC]
Belaid, Fateh [Auteur]
Lille économie management - UMR 9221 [LEM]
Benkraiem, Ramzi [Auteur]
Audencia Business School
Ramdani, Boumediene [Auteur]
Qatar University
Guesmi, Khaled [Auteur]
EDC Paris Business School [EDC]
Journal title :
Annals of Operations Research
Pages :
771-793
Publisher :
Springer Verlag
Publication date :
2023-11
ISSN :
0254-5330
Keyword(s) :
Multi-criteria classification
sorting
clustering
sorting
clustering
English keyword(s) :
Multicriteria Classification Sorting Clustering Methods Modelling
Bibliometrics
Multicriteria
Classification
Sorting
Clustering
Methods
Modelling
Bibliometrics
Multicriteria
Classification
Sorting
Clustering
Methods
Modelling
HAL domain(s) :
Statistiques [stat]/Méthodologie [stat.ME]
French abstract :
Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, ...
Show more >Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.Show less >
Show more >Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.Show less >
English abstract : [en]
Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, ...
Show more >Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.Show less >
Show more >Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.Show less >
Language :
Anglais
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