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Geometrical codification for clustering mixed categorical and numerical databases

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Geometrical codification for clustering mixed categorical and numerical databases

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Barceló Rico, F.; Diez, J. (2012). Geometrical codification for clustering mixed categorical and numerical databases. Journal of Intelligent Information Systems. 39(1):167-185. doi:10.1007/s10844-011-0187-y

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/93511

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Title: Geometrical codification for clustering mixed categorical and numerical databases
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica
Issued date:
Abstract:
[EN] This paper presents an alternative to cluster mixed databases. The main idea is to propose a general method to cluster mixed data sets, which is not very complex and still can reach similar levels of performance of ...[+]
Subjects: Clustering , Codification error , Data conversion , k-means , Mixed data , Categorical attributes , General method , Input matrices , k-Means algorithm , Mixed database , Spherical coordinates , Benchmarking , Database systems , Clustering algorithms
Copyrigths: Reserva de todos los derechos
Source:
Journal of Intelligent Information Systems. (issn: 0925-9902 )
DOI: 10.1007/s10844-011-0187-y
Publisher:
SPRINGER
Publisher version: http://doi.org/10.1007/s10844-011-0187-y
Thanks:
The authors acknowledge the partial funding of this work by the National projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01.
Type: Artículo

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