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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/29| Pattern-based clustering using unsupervised decision trees | |
| ANDRES EDUARDO GUTIERREZ RODRÍGUEZ | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Patter mining Pattern-based clustering Clustering Mixed Datasets | |
| In clustering, providing an explanation of the results is an important task. Pattern-based clustering algorithms provide, in addition to the list of objects belonging to each cluster, an explanation of the results in terms of a set of patterns that describe the objects grouped in each cluster. It makes these algorithms very attractive from the practical point of view; however, patternbased clustering algorithms commonly have a high computational cost in the clustering stage. Moreover, the most recent algorithms proposed within this approach, extract patterns from numerical datasets by applying an a priori discretization process, which may cause information loss. In this thesis, we propose new algorithms for extracting only a subset of patterns useful for clustering, from a collection of diverse unsupervised decision trees induced from a dataset. Additionally, we propose a new clustering algorithm based on these patterns. | |
| Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| 23-11-2015 | |
| Tesis de doctorado | |
| Inglés | |
| Público en general | |
| Gutierrez-Rodriguez A. E. | |
| SISTEMAS DE RECONOCIMIENTO DE CARACTERES | |
| Aparece en las colecciones: | Doctorado en Ciencias Computacionales |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| GutierrezRoAE.pdf | 1.41 MB | Adobe PDF | Visualizar/Abrir |