Comparing Classical Criteria For Selecting Intra-Class Correlated Features For Three-Mode Three-Way Data

Many unsupervised learning tasks involve data sets with both continuous and categorical attributes. One possible approach to clustering such data is to assume that the data to be clustered come from a finite mixture of populations. There has been extensive use of mixtures where the component distributions are multivariate normal and where the data would be described as two mode two way data. The finite mixture model can also be used to cluster three way data. The mixture model approach requires the specification of the number of components to be fitted to the model and the form of the density functions of the underlying components.
This talk illustrates the performance of several commonly used model selection criteria in selecting both the number of components and the form of the correlation structure amongst the attributes when fitting a mixture model to the finite mixture model to cluster three way data containing mixed categorical and continuous attributes