Clasificación automática simbólica por medio de algoritmos genéticos
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| Autoři: | , |
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| Médium: | artículo original |
| Stav: | Versión publicada |
| Datum vydání: | 2009 |
| Popis: | This paper presents a variant in the methods for clustering: a genetic algorithm for clustering through the tools of symbolic data analysis. Their implementation avoids the troubles of clustering classical methods: local minima and dependence of data types: numerical vectors (continuous data type). The proposed method was programmed in MatLab©R and it uses an interesting operator of encoding. We compare the clusters by their intra-clusters inertia. We used the following measures for symbolic data types: Ichino-Yaguchi dissimilarity measure, Gowda-Diday dissimilarity measure, Euclidean distance and Hausdorff distance. |
| Země: | Portal de Revistas UCR |
| Instituce: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Jazyk: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/307 |
| On-line přístup: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/307 |
| Klíčové slovo: | Clustering symbolic analysis k-means genetic algorithm optimization Clasificación automática análisis simbólico algoritmos genéticos optimización |