| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560535 |
| ISSN: | 1405-5546 |
| Autores: | Ouali, Mohammed1 Mahdi, Walid1 Gharbaoui, Radhwane3 Medjahed, Seyyid Ahmed3 |
| Instituciones: | 1Taif University, College of Computers and Information Technology, Arabia Saudí 2Thales Canada Inc., Canadá 3Université des Sciences et Technologie, Département d’Informatique, Algerie |
| Año: | 2020 |
| Periodo: | Jul-Sep |
| Volumen: | 24 |
| Número: | 3 |
| Paginación: | 1075-1091 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Clustering methods are used for identifying groups of similar objects considered as homogenous set. Unfortunately, analytic performance evaluation of clustering methods is a difficult task because of their ad-hoc nature. In this paper, we propose a new test case generator of artificial data for 2 dimensional Gaussian mixtures. The proposed generator has two interesting advantages: the first one is its ability to produce simulated mixture for any number of components, while the second one resides in the fact that it formally quantifies the overlap rate which allows us to add some complexity to the data. Clustering algorithms and validity indices behavior is also analyzed by changing the overlap rate between clusters. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Clustering algorithms, Unsupervised learning, Gaussian mixture, Gaussian components overlap, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
