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MCClusteringSM: An approach for the Multicriteria Clustering Problem based on a Credibility Similarity Measure

Lugo Medrano Cesar¹, Gastelum Chavira Diego Alonso², Valdez Lafarga Octavio³ and Velarde Cervantes Jose Luis⁴

1. Doctoral Program in Business Analytics, Universidad Autonoma de Occidente
   Blvd. Lola Beltran y Blvd Rolando Arjona, CP80020 Culiacan, Mexico
   cesar.lugo@uadeo.mx
2. Department of Economic-Administrative Sciences, Universidad Autonoma de Occidente
   Blvd. Lola Beltran y Blvd Rolando Arjona, CP80020 Culiacan, Mexico
   diego.chavira@uadeo.mx
3. Department of Technology and Engineering, Universidad Autonoma de Occidente
   Blvd. Lola Beltran y Blvd Rolando Arjona, CP80020 Culiacan, Mexico
   octavio.valdezl@uadeo.mx
4. Faculty of Computer Science, Universidad Autonoma de Sinaloa
   Josefa Ortiz de Dominguez SN, Cd. Universitaria, CP80013 Culiacan, Mexico
   jl.velarde@info.uas.edu.mx

Abstract

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.

Key words

MCDA, Multicriteria Clustering Problem, Similarity Measure

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230302033M

Publication information

Volume 21, Issue 3 (June 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
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How to cite

Cesar, L. M., Alonso, G. C. D., Octavio, V. L., Luis, V. C. J.: MCClusteringSM: An approach for the Multicriteria Clustering Problem based on a Credibility Similarity Measure. Computer Science and Information Systems, Vol. 21, No. 3, 967-997. (2024), https://doi.org/10.2298/CSIS230302033M