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Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017
This paper introduces a parameterless approach named MCDA: Multidimensional Communities Detection Algorithm. MCDA adopts a local search mechanism which is inspired from the label propagation principle. To this end, we design a novel propagation rule that exploits the most frequently used interaction dimensions among neighbors as an additional ...
Oualid Boutemine, Mohamed Bouguessa
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This paper introduces a parameterless approach named MCDA: Multidimensional Communities Detection Algorithm. MCDA adopts a local search mechanism which is inspired from the label propagation principle. To this end, we design a novel propagation rule that exploits the most frequently used interaction dimensions among neighbors as an additional ...
Oualid Boutemine, Mohamed Bouguessa
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Journal of Modelling in Management
Multicriteria decision analysis (MCDA) has become increasingly essential for decision-making in complex environments. In response to this need, the pyDecision library, implemented in Python and available at https://bit.ly/3tLFGtH, has been developed ...
Valdecy Pereira +3 more
semanticscholar +1 more source
Multicriteria decision analysis (MCDA) has become increasingly essential for decision-making in complex environments. In response to this need, the pyDecision library, implemented in Python and available at https://bit.ly/3tLFGtH, has been developed ...
Valdecy Pereira +3 more
semanticscholar +1 more source
2015
The implementation of GIS-based MCDA has evolved in step with advances in GIS and information technology in general. Historically, implementations were isolated through the use of specific scripting or programming languages and incompatible GIS data structures.
Jacek Malczewski, Claus Rinner
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The implementation of GIS-based MCDA has evolved in step with advances in GIS and information technology in general. Historically, implementations were isolated through the use of specific scripting or programming languages and incompatible GIS data structures.
Jacek Malczewski, Claus Rinner
openaire +1 more source
2015
This chapter traces the roots of GIS-MCDA and focuses on recent developments in GIS-MCDA research. It describes two research traditions underlying the development of GIS-MCDA: Operations Research and Management Sciences (OR/MS) and landscape architecture/planning.
Jacek Malczewski, Claus Rinner
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This chapter traces the roots of GIS-MCDA and focuses on recent developments in GIS-MCDA research. It describes two research traditions underlying the development of GIS-MCDA: Operations Research and Management Sciences (OR/MS) and landscape architecture/planning.
Jacek Malczewski, Claus Rinner
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1999
The underlying concepts of MAUT, SMART, AHP, preference cones, ZAPROS, and outranking methods are compared. Learning systems are considered. The learning view is that decision makers initially do not fully understand all of the criteria that are important.
D. L. Olson +2 more
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The underlying concepts of MAUT, SMART, AHP, preference cones, ZAPROS, and outranking methods are compared. Learning systems are considered. The learning view is that decision makers initially do not fully understand all of the criteria that are important.
D. L. Olson +2 more
openaire +1 more source
Geometrical representations for MCDA
European Journal of Operational Research, 1988Abstract In this paper geometrical representations for multicriteria decision problems are proposed. This new approach provides assistance to understand the conflictual aspects of the criteria and to tackle the problem of the weights associated to them.
Mareschal, Bertrand, Brans, Jean Pierre
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Theoretical Foundations of MCDA
2017Decision-makers in the healthcare sector face a global challenge of developing robust, evidence-based methods for making decisions about whether to fund, cover, or reimburse medical technologies. Allocating scarce resources across technologies is difficult because a range of criteria are relevant to a healthcare decision, including the effectiveness ...
Dean A. Regier, Stuart Peacock
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