Add to ORKGIn cluster analysis it is generally assumed that one single cluster structure is contained in a data matrix, and that this structure may be confined to a subset of the observed variables. This paper investigates a new solution that simultaneously selects the relevant variables and discovers multiple cluster structures from possibly dependent subsets of variables. The basic idea is to recast the problem as a model comparison problem in which conditional independence assumptions are introduced using multivariate regression models with correlated and non-normal error terms. A stepwise procedure for selecting a locally optimal model is also proposed. Results obtained from a Monte Carlo study are briefly described
International audienceIn model based clustering, it is often supposed that only one clustering laten...
We compare two major approaches to variable selection in clustering: model selection and regularizat...
Abstract. It is common to perform clustering methods independently on dierent data sets while (i) al...
In cluster analysis it is generally assumed that one single cluster structure is contained in a data...
There is an interest in the problem of identifying different partitions of a given set of units obta...
This paper deals with the problem of identifying different partitions of a given set of units obtain...
In multivariate datasets, multiple clustering solutions can be obtained, based on different subsets ...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
International audienceIn model-based clustering, each cluster is modelled by a parametrised probabil...
In cluster analysis, it could be useful to interpret the obtained partition with respect to external...
The problem of variable clustering is that of estimating groups of similar components of a p-dimensi...
In cluster analysis, it can be useful to interpret the partition built from the data in the light of...
Model-based cluster analysis is a common clustering method. Unlike the classical clustering methods,...
International audienceIn the framework of model-based clustering, a model allowing several latent cl...
open3noFirst Online: 12 January 2017In the framework of cluster analysis based on Gaussian mixture m...
International audienceIn model based clustering, it is often supposed that only one clustering laten...
We compare two major approaches to variable selection in clustering: model selection and regularizat...
Abstract. It is common to perform clustering methods independently on dierent data sets while (i) al...
In cluster analysis it is generally assumed that one single cluster structure is contained in a data...
There is an interest in the problem of identifying different partitions of a given set of units obta...
This paper deals with the problem of identifying different partitions of a given set of units obtain...
In multivariate datasets, multiple clustering solutions can be obtained, based on different subsets ...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
International audienceIn model-based clustering, each cluster is modelled by a parametrised probabil...
In cluster analysis, it could be useful to interpret the obtained partition with respect to external...
The problem of variable clustering is that of estimating groups of similar components of a p-dimensi...
In cluster analysis, it can be useful to interpret the partition built from the data in the light of...
Model-based cluster analysis is a common clustering method. Unlike the classical clustering methods,...
International audienceIn the framework of model-based clustering, a model allowing several latent cl...
open3noFirst Online: 12 January 2017In the framework of cluster analysis based on Gaussian mixture m...
International audienceIn model based clustering, it is often supposed that only one clustering laten...
We compare two major approaches to variable selection in clustering: model selection and regularizat...
Abstract. It is common to perform clustering methods independently on dierent data sets while (i) al...