Summary
This paper considers the problem of choosing the number of component clusters of individuals, determining the variables which are contributing to the differences between the clusters using all possible subset selection of variables, and detecting outliers or extreme observations across the clustering alternatives in one expert-system simultaneously within the context of the standard mixture of multivariate normal distributions. This is achieved by introducing and deriving a new informational measure of complexity (ICOMP) criterion of the estimated inverse-Fisher information matrix (IFIM) developed by Bozdogan as an alternative to Akaike’ s information criterion (AIC), and Bozdogan’s CAIC for the mixture-model. A numerical example is shown on a real data set to illustrate the significance of these validity functionals.
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Bozdogan, H. (1994). Choosing the Number of Clusters, Subset Selection of Variables, and Outlier Detection in the Standard Mixture-Model Cluster Analysis. In: Diday, E., Lechevallier, Y., Schader, M., Bertrand, P., Burtschy, B. (eds) New Approaches in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51175-2_19
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DOI: https://doi.org/10.1007/978-3-642-51175-2_19
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