Utilize este identificador para referenciar este registo: 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
Data: 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.
Peer review: yes
URI: http://hdl.handle.net/10451/5659
http://www.ibib.waw.pl/bbe/bbefulltext/BBE_29_2_009_FT.pdf
ISSN: 0208-5216
Aparece nas colecções:FM-IMP-Artigos em Revistas Internacionais

Ficheiros deste registo:
Ficheiro Descrição TamanhoFormato 
Data_cluster_analysis.pdf143,7 kBAdobe PDFVer/Abrir    Acesso Restrito. Solicitar cópia ao autor!


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.