Model-based cluster and discriminant analysis with the MIXMOD software

Model-based cluster and discriminant analysis with the MIXMOD software

The mixmod (mixture modeling) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM), and it is possible to combine these to yield different strategies for obtaining a sensible maximum for the likelihood (or co...

Xemgaus: Software for Model-based Cluster and Discriminant Analysis

On Model-Based Clustering, Classification, and Discriminant Analysis

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

cluster analysis for determination of the hydrologic homogeneitym, assessment with discriminant analysis and andrew curves in karkheh basin

usually sufficient number of data of the hydrological variables is not available for frequency analysis. therefore, through the method of regional analysis the sample data are used for fitting with probability distributions and for probability theories regarding statistical population. the results of regional analysis would be valid, in case of the study region to be homogeneous in the view of ...

Variable selection in model-based discriminant analysis

A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem. Three different roles for each possible predictor are considered: a variable can be a relevant classification predictor or not, and the irrelevant classification variables can be linearly dependent on a part of the relevant predictors o...