Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values
AIC criterion.
Adjusted Rand Index
BIC criterion.
Extract the parameters
Extract the parameters
Extract the partition or the probabilities of classification
Extract the partition or the probabilities of classification
ICL criterion
MICL criterion
Plots of an instance of VSLCMresults
Prediction of the cluster memberships
Print function.
Summary function.
Variable selection and clustering.
Imputation of missing values
Variable Selection for Model-Based Clustering of Mixed-Type Data Set w...
Shiny app for analyzing results from VarSelCluster
Constructor of VSLCMcriteria class
Constructor of VSLCMdata class
Constructor of VSLCMmodel class
Constructor of VSLCMparam class
Constructor of VSLCMparamCategorical class
Constructor of VSLCMparamContinuous class
Constructor of VSLCMparamInteger class
Constructor of VSLCMpartitions class
Constructor of VSLCMresults class
Constructor of VSLCMstrategy class
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.