Clasificación automática simbólica por medio de algoritmos genéticos
保存先:
| 著者: | , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2009 |
| その他の書誌記述: | 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. |
| 国: | Portal de Revistas UCR |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| 言語: | Español |
| OAI Identifier: | oai:archivo.portal.ucr.ac.cr:article/307 |
| オンライン･アクセス: | https://archivo.revistas.ucr.ac.cr/index.php/matematica/article/view/307 |
| キーワード: | Clustering symbolic analysis k-means genetic algorithm optimization Clasificación automática análisis simbólico algoritmos genéticos optimización |