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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
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
ISSN: 0208-5216
Appears in Collections:FM-IMP-Artigos em Revistas Internacionais

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