dc.creatorANDRES EDUARDO GUTIERREZ RODRÍGUEZ
dc.date2015-11-23
dc.date.accessioned2023-07-25T16:20:44Z
dc.date.available2023-07-25T16:20:44Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/29
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7805252
dc.descriptionIn 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstituto Nacional de Astrofísica, Óptica y Electrónica
dc.relationcitation:Gutierrez-Rodriguez A. E.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Reconcimiento de patrones/Patter mining
dc.subjectinfo:eu-repo/classification/Agrupación de patrones/Pattern-based clustering
dc.subjectinfo:eu-repo/classification/Agrupación/Clustering
dc.subjectinfo:eu-repo/classification/Datos mixtos/Mixed Datasets
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/330405
dc.subjectinfo:eu-repo/classification/cti/330405
dc.titlePattern-based clustering using unsupervised decision trees
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.audiencegeneralPublic


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