Clustering via ant colonies: Parameter analysis and improvement of the algorithm
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| Autores: | , , |
|---|---|
| Formato: | capítulo de libro |
| Fecha de Publicación: | 2020 |
| Descripción: | An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant colony method. We performed a simulation study in order to evaluate the method with a Monte Carlo experiment that controls some sensitive parameters of the clustering problem. After some tuning of the parameters, the method has also been applied to some benchmark real-data sets. Encouraging results were obtained in nearly all cases. |
| País: | Kérwá |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/84940 |
| Acceso en línea: | https://link.springer.com/chapter/10.1007%2F978-981-15-2700-5_16 https://hdl.handle.net/10669/84940 |
| Palabra clave: | Clustering Ant colony optimization Combinatorial optimization Within-class inertia |