An extension of PROMETHEE to interval clustering

Sarrazin, Renaud; Smet, Yves De; Rosenfeld, John L.

doi:10.1016/j.omega.2017.09.001

Cited by 33 publications

(25 citation statements)

References 12 publications

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“…In fact, research in MCC often proposes totally ordered clustering [17,20,21,22]. This constraint may not be adapted to the natural structure of the data, which may present quite distinct clusters but without any preferential relationship between them, as mentioned above for clusters A and B in Figure 1.…”

Section: Multicriteria Clustering (Mcc) In Mcdamentioning

confidence: 99%

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Assessing partially ordered clustering in a multicriteria comparative context

Rosenfeld

Smet

Debeir

et al. 2021

Pattern Recognition

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This study considers the task of clustering for data characterized by peculiar quantitative features in that they express performance according to different indicators or criteria. Performance is supposed to be optimized in one way or the other, i.e. maximized or minimized. This peculiar type of data introduces a comparative context that is not generally taken into account in the field of pattern recognition, in general, and clustering, in particular. In the present study, we introduce different concepts and develop tools that facilitate the evaluation of data partitions in this comparative context leading to the consideration of asymmetric preference relationships between objects and between clusters. We show their usefulness on the basis of artificial data and also by analyzing the results produced on real data by means of clustering methods.

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Section: Multicriteria Clustering (Mcc) In Mcdamentioning

confidence: 99%

“…supplementary material). These methods were "naturally" extended for clustering purposes in MCC algorithms that generate partitions of totally ordered clusters [17,20,21,22]. In these MCC methods, this strong order constraint takes precedence over the compactness and separability properties of clusters.…”

Section: World Happiness Report 2019 Datasetmentioning

confidence: 99%

Assessing partially ordered clustering in a multicriteria comparative context

Rosenfeld

Smet

Debeir

et al. 2021

Pattern Recognition

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“…Even the PROMETHEE method is commonly used to ranking problems, sorting and clustering approaches are present [63][64][65][66][67], working with proposals where the alternatives are allocated in clusters, ranking them into each class, or models where it is possible to obtain a partial or complete ranking of clusters.…”

Section: Variants Of the Promethee Methodsmentioning

confidence: 99%

PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

et al. 2021

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This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems.

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“…The K-Means algorithm has become a standard tool for data analysts in a wide variety of fields [37,45]. It assigns one of the 𝐾 clusters to a set of N points by minimizing the distance, according to the selected distance measure, from each point to the center of the cluster assigned to it [35].…”

Section: Clusteringmentioning

confidence: 99%

Stroke care networks and the impact on quality of care

Schoenfelder

Zarrin

Griesbaum

et al. 2021

Health Care Manag Sci

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Lack of rapidly available neurological expertise, especially in rural areas, is one of the key obstacles in stroke care. Stroke care networks attempt to address this challenge by connecting hospitals with specialized stroke centers, stroke units, and hospitals of lower levels of care. While the benefits of stroke care networks are well-documented, travel distances are likely to increase when patients are transferred almost exclusively between members of the same network. This is particularly important for patients who require mechanical thrombectomy, an increasingly employed treatment method that requires equipment and expertise available in specialized stroke centers. This study aims to analyze the performance of the current design of stroke care networks in Bavaria, Germany, and to evaluate the improvement potential when the networks are redesigned to minimize travel distances. To this end, we define three fundamental criteria for assessing network design performance: 1) average travel distances, 2) the populace in the catchment area relative to the number of stroke units, and 3) the ratio of stroke units to lower-care hospitals. We generate several alternative stroke network designs using an analytical approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in Bavaria via simulation. The results show that the current network design could be significantly improved concerning the average travel distances. Moreover, the existing networks are unnecessarily imbalanced when it comes to their number of stroke units per capita and the ratio of stroke units to lower-care hospitals.

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An extension of PROMETHEE to interval clustering

Cited by 33 publications

References 12 publications

Assessing partially ordered clustering in a multicriteria comparative context

Assessing partially ordered clustering in a multicriteria comparative context

PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

Stroke care networks and the impact on quality of care

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