Universidade de Lisboa Repositório da Universidade de Lisboa

Repositório da Universidade de Lisboa >
Faculdade de Medicina (FM) >
Instituto de Medicina Preventiva (FM-IMP) >
FM-IMP-Artigos em Revistas Internacionais >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10451/5659

Título: Measuring similarity of complex and heterogeneous data in clustering of large data sets
Autor: Nicolau, Helena Bacelar
Nicolau, Fernando
Sousa, Áurea
Nicolau, Leonor Bacelar
Palavras-chave: Cluster analysis
Different type variables
Similarity coefficient
Three-way data
Statistics
Issue Date: 2009
Editora: Polish Academy of Sciences
Citação: Biocybernetics and Biomedical Engineering 2009;29(2):9–18
Resumo: Cluster analysis or classification usually concerns a set of exploratory multivariate data analysis methods and techniques for finding a clustering structure on a dataset. That may refer either to groups of statistical data units or to groups of variables. In this work we deal with a generalization of this paradigm concerning clustering of complex data described by three different types of variables, frequently present in a three-way context. We obtain compatible versions of the same affinity coefficient for measuring similarity between statistical data units described by those three types of variables. A global generalized similarity coefficient is analyzed for such kind of mixed data, often arising in data mining or knowledge mining.
Arbitragem científica: yes
URI: http://hdl.handle.net/10451/5659
http://www.ibib.waw.pl/bbe/bbefulltext/BBE_29_2_009_FT.pdf
ISSN: 0208-5216
Appears in Collections:FM-IMP-Artigos em Revistas Internacionais

Files in This Item:

File Description SizeFormat
Data_cluster_analysis.pdf143,7 kBAdobe PDFView/Open
Restrict Access. You can request a copy!
Statistics
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

  © Universidade de Lisboa / SIBUL
Alameda da Universidade | Cidade Universitária | 1649-004 Lisboa | Portugal
Tel. +351 217967624 | Fax +351 217933624 | repositorio@reitoria.ul.pt - Feedback - Statistics
DeGóis
  Estamos no RCAAP Governo Português separator Ministério da Educação e Ciência   Fundação para a Ciência e a Tecnologia

Financiado por:

POS_C UE