MTO Colloquium Monday, November 24 at 12:45 in WZ 204 Lynette A. Hunt (University of Waikato, Hamilton, New Zealand) ‘Model selection for the Multimix class of Mixture Models’ The mixture approach to clustering is a model based approach to clustering that requires the specification of the form of the component distributions and the number of groups that are to be fitted to the model. The Multimix class of mixture models (Hunt & Jorgensen 1999) enables the clustering of data that have both categorical and continuous attributes. However with this approach to clustering data, the user also has to decide on the correlation structure that is to be incorporated into the model. We investigate the performance of some commonly used model selection criteria in the selection of an appropriate model when using the finite mixture model to cluster data containing mixed categorical and continuous attributes. The performance of these criteria in selecting both the form of the correlation structure and the number of components to be used in the model is assessed using simulated data and a medical data set. We found that the Bayesian information criterion and the integrated classification likelihood can detect the number of groups to be fitted to a mixture model when the correct partitioning is included in the model whilst the AIC and CLC perform in a less satisfactory way. However we found that caution needs to be used when using informati on criteria to select your model. Hunt, L.A. & Jorgensen, M.A., (1999). ‘Mixture Model Clustering: a brief introduction to the MULTIMIX program’. Australian and New Zealand Journal of Statistics, 41, No. 2, 153 -171.