Description A function which implements variable selection methodology for model-based cluster-ing which allows to find the (locally) optimal subset of vari-ables in a dataset that have group/cluster information. A greedy or head-long search can be used, either in a forward-backward or backward-forward direc-tion, with or without sub-sampling at the hierarchical clustering stage for starting Mclust mod-els. By default the algorithm uses a sequential search, but parallelization is also available
International audienceThe MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new pro...
The problem of cluster-grouping is defined. It integrates subgroup discovery, mining correlated patt...
Abstract: The process of selecting appropriate feature done by finding of sub module of wanted featu...
Description A collection of functions which (i) assess the quality of variable subsets as surro-gate...
This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchi...
Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mix...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
Description Orders panels in scatterplot matrices and parallel coordinate displays by some merit ind...
ing (HMAC) along with their parallel implementation (PHMAC) over several proces-sors. These model-ba...
Data mining, the extraction of hidden predictive information from large databases, is a powerful new...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Description The package implements several ABC algorithms for performing parameter estimation and mo...
The greed package implements the general and flexible framework of arXiv:2002.11577 for model-based ...
Description Optimal two and three stage designs monitoring time-to-event endpoints at a specified ti...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunc...
International audienceThe MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new pro...
The problem of cluster-grouping is defined. It integrates subgroup discovery, mining correlated patt...
Abstract: The process of selecting appropriate feature done by finding of sub module of wanted featu...
Description A collection of functions which (i) assess the quality of variable subsets as surro-gate...
This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchi...
Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mix...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
Description Orders panels in scatterplot matrices and parallel coordinate displays by some merit ind...
ing (HMAC) along with their parallel implementation (PHMAC) over several proces-sors. These model-ba...
Data mining, the extraction of hidden predictive information from large databases, is a powerful new...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Description The package implements several ABC algorithms for performing parameter estimation and mo...
The greed package implements the general and flexible framework of arXiv:2002.11577 for model-based ...
Description Optimal two and three stage designs monitoring time-to-event endpoints at a specified ti...
This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunc...
International audienceThe MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new pro...
The problem of cluster-grouping is defined. It integrates subgroup discovery, mining correlated patt...
Abstract: The process of selecting appropriate feature done by finding of sub module of wanted featu...