Octavio Valdez Lafarga
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Octavio Valdez Lafarga has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Cesar Lugo Medrano
Doctoral Program in Business Analytics, Universidad Autonoma de Occidente, Culiacan, Mexico
1 shared publication - Diego Alonso Gastélum-Chavira
Universidad Autonoma de Occidente
1 shared publication Jose Luis Velarde Cervantes
Faculty of Computer Science, Universidad Autonoma de Sinaloa Josefa Ortiz de Dominguez SN, Cd. Universitaria, Culiacan, Mexico
1 shared publication
Latest Publications
- Open Access
- 1 January 2024
- Published by National Library of Serbia in Computer Science and Information Systems
- Vol. 21 (3) , 1147-1177
MCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure
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.
- 1 January 2024
- Published by National Library of Serbia in Computer Science and Information Systems
- Vol. 21 (3) , 1147-1177
Open AccessMCClusteringSM: An approach for the multicriteria clustering problem based on a credibility similarity measure
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.