Abstract
In the field of multicriteria decision aid, considerable attention has been paid to supervised classification problems where the purpose is to assign alternatives into predefined ordered classes. In these approaches, often referred to as sorting methods, it is usually assumed that classes are either known a priori or can be identified by the decision maker. On the other hand, when the objective is to identify groups (clusters) of alternatives sharing similar characteristics, the problem is known as a clustering problem, also called an unsupervised learning problem. This paper proposes an agglomerative clustering method based on a crisp outranking relation. The method regroups alternatives into partially ordered classes, based on a quality of partition measure which reflects the percentage of pairs of alternatives that are compatible with a decision-maker’s multicriteria preference model.
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Acknowledgments
This work has been partially supported by Fundação para a Ciência e a Tecnologia (FCT) under project PEst-/EEI/UI0308/2011 and FEUC’s 40th Anniversary incentive.
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Rocha, C., Dias, L.C. MPOC: an agglomerative algorithm for multicriteria partially ordered clustering. 4OR-Q J Oper Res 11, 253–273 (2013). https://doi.org/10.1007/s10288-013-0228-1
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DOI: https://doi.org/10.1007/s10288-013-0228-1
Keywords
- Multi-criteria decision aiding (MCDA)
- Sorting problem
- Clustering
- Outranking relations
- Agglomerative algorithm
- Partial ranking