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Bacelar-Nicolau H., Nicolau F. F., Sousa A., Bacelar-Nicolau L.

Biocybernetics and Biomedical Engineering

2009

Vol. 29, no. 2

9-18

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.

On the generalised affinity coefficient for complex data

Bacelar-Nicolau H.

Biocybernetics and Biomedical Engineering

2002

Vol. 22, no. 1

31-42

This paper concerns the affinity coefficient and extensions for maesuring the similarity between data units in classification, when we are dealing with large and complex databases. More precisely we refer to the extended weighted affinity coefficient and its role in hierarchical classification, when we are dealing with a generalised data table where the cells can contain a set of values, describing a probability distribution, a histogram (frequency distribution), or integer frequencies, for instance, instead one single value. Here we study the case of frequency distributions, since in our approach the other cases appear to be derived as a generalisation (integer frequencies, real data, for instance) or else as a particular case (binary data, ordinal data) of this one. Either the weighted affinity coefficient or the probabilistic associated coefficients can be extended, in the clustering viewpoint, to hierarchical (and non-hierarchical) aggregation criteria and aggregation adaptive (parametric) families. An application to a real case is presented.