Bacelar-Nicolau, HelenaNicolau, Fernando C.Sousa, ÁureaBacelar-Nicolau, Leonor2014-02-032014-02-032009Bacelar-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 0208-5216.0208-5216http://hdl.handle.net/10400.3/2664Copyright © 2009 Polish Academy of Sciences.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.engCluster AnalysisDifferent Type VariablesSimilarity CoefficientThree-way Datajournal article2014-01-28