Clasificacion automática simbólica por medio de algoritmos genéticos

    This paper presents a variant in the methods for clustering: a genetic algorithm forclustering through the tools of symbolic data analysis. Their implementation avoidsthe troubles of clustering classical methods: local minima and dependence of datatypes: numerical vectors (continuous data type)....

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Autores Principales: Fernández Jiménez, Fabio, Murillo Fernández, Álex
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/307
http://hdl.handle.net/10669/12964
Sumario:     This paper presents a variant in the methods for clustering: a genetic algorithm forclustering through the tools of symbolic data analysis. Their implementation avoidsthe troubles of clustering classical methods: local minima and dependence of datatypes: numerical vectors (continuous data type).    The proposed method was programmed in MatLab R and it uses an interestingoperator of encoding. We compare the clusters by their intra-clusters inertia. We usedthe following measures for symbolic data types: Ichino-Yaguchi dissimilarity measure,Gowda-Diday dissimilarity measure, Euclidean distance and Hausdorff distance.