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Measuring similarity of complex and heterogeneous data in clustering of large data sets
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Orientador(es)
Resumo(s)
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.
Palavras-chave
Cluster Analysis Different Type Variables Similarity Coefficient Three-way Data
Contexto Educativo
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.
Editora
Polish Academy of Sciences