Towards multicriteria analysis: A new clustering approach

Rouba, Baroudi; Safia, Nait Bahloul

doi:10.1109/icmwi.2010.5648063

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The strength of this method lies on its capacity to produce three clustering schemes, nominal, relational with partial order or complete order. A new distance measure based on both preference information (preference, indifference and incomparability relations) and the SOKAL and MICHENER similarity index is proposed by [39]. These measures are used to generate four clustering partitions via the k-means algorithm.…”

Section: Ordered Clustering (Order Relation On the Clusters)mentioning

confidence: 99%

“…It is possible to notice that ordered multi-criteria clustering methods can be divided in two categories according to their process. Some methods are based on a two-step process, the first one performs a classical clustering, and the second one allows to refine the final distribution [36], [37], [38], [39], [40] and [41]. The second category includes recent methods, those are based on an extension of the K-means algorithm by using outranking methods, notably the PROMETHEE method [42], [43], [44], [45] and [1].…”

Section: Ordered Clustering (Order Relation On the Clusters)mentioning

confidence: 99%

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Multi-criteria clustering analysis for large-scale public transport performance diagnosis

TOUATI,

BOUAMRANE,

HAMDADOU

2023

IJCDS

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Public transport is a key factor for the global economy; therefore, it has always been a directive of governments to report on its performance to authorities and public. The purpose of the present study is providing a large-scale performance diagnosis dashboard for bus public transport systems to deal with multi-criteria context. The proposed dashboard can assist transportation authorities in undertaking a comprehensive performance evaluation both at route and system level. The methodology of this study is an integration of (i) ordered multi-criteria clustering method based on the K-means algorithm and the FLOWSORT outranking method, (ii) weighted average and (iii) PROMETHEE parameters-based single-criteria analysis. Inspired by an interesting route level evaluation methodology from recent research, a template is generated to illustrate the proposed approach. Outcomes are promising for investing in other multicriteria clustering methods to deal with large-scale performance evaluation at both route and system levels. The proposed approach can fit any evaluation model based on performance criteria. It allows a detailed presentation of the diagnosis in spite of the large-scale context, which eases the optimization process.

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Section: Ordered Clustering (Order Relation On the Clusters)mentioning

confidence: 99%

Section: Ordered Clustering (Order Relation On the Clusters)mentioning

confidence: 99%

Multi-criteria clustering analysis for large-scale public transport performance diagnosis

TOUATI,

BOUAMRANE,

HAMDADOU

2023

IJCDS

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Formalizing and solving the problem of clustering in MCDA

Meyer

Olteanu

2013

European Journal of Operational Research

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MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure

Medrano,

Alonso,

Lafarga

et al. 2024

COMSIS J

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Multicriteria clustering problem has been studied and applied scarcely. When a multicriteria clustering problem is tackled with an outranking approach, it is necessary to include preferences of decision makers on the raw dataset, e.g., weights and thresholds of the evaluation criteria. Then, it is necessary to conduct a process to obtain a comprehensive model of preferences represented in a fuzzy or crisp outranking relation. Subsequently, the model can be exploited to derive a multicriteria clustering. This work presents an exhaustive search approach using a credibility similarity measure to exploit a fuzzy outranking relation to derive a multicriteria clustering. The work includes two experimental designs to evaluate the performance of the algorithm. Results show that the proposed method has good performance exploiting fuzzy outranking relations to create the clusterings.

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Towards multicriteria analysis: A new clustering approach

Cited by 3 publications

References 14 publications

Multi-criteria clustering analysis for large-scale public transport performance diagnosis

Multi-criteria clustering analysis for large-scale public transport performance diagnosis

Formalizing and solving the problem of clustering in MCDA

MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure

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