Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.3/2664
Título: Measuring similarity of complex and heterogeneous data in clustering of large data sets
Autor: Bacelar-Nicolau, Helena
Nicolau, Fernando C.
Sousa, Áurea
Bacelar-Nicolau, Leonor
Palavras-chave: Cluster Analysis
Different Type Variables
Similarity Coefficient
Three-way Data
Data: 2009
Editora: Polish Academy of Sciences
Citação: Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, Leonor (2009). "Measuring similarity of complex and heterogeneous data in clustering of large data sets", Biocybernetics and Biomedical Engineering, 29(2), 9-18. ISSN 0208-5216.
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.
Descrição: Copyright © 2009 Polish Academy of Sciences.
Peer review: yes
URI: http://hdl.handle.net/10400.3/2664
ISSN: 0208-5216
Versão do Editor: http://www.ibib.waw.pl/bbe/bbefulltext/BBE_29_2_009_FT.pdf
Aparece nas colecções:DME - Artigos em Revistas Internacionais / Articles in International Journals

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