Título
Pattern-based clustering using unsupervised decision trees
Autor
ANDRES EDUARDO GUTIERREZ RODRÍGUEZ
Nivel de Acceso
Acceso Abierto
Materias
Patter mining - (RECONCIMIENTO DE PATRONES) Pattern-based clustering - (AGRUPACIÓN DE PATRONES) Clustering - (AGRUPACIÓN) Mixed Datasets - (DATOS MIXTOS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) SISTEMAS DE RECONOCIMIENTO DE CARACTERES - (CTI)
Resumen o descripción
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.
Editor
Instituto Nacional de Astrofísica, Óptica y Electrónica
Fecha de publicación
23 de noviembre de 2015
Tipo de publicación
Tesis de doctorado
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Público en general
Sugerencia de citación
Gutierrez-Rodriguez A. E.
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
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