Add to ORKGTwo methods for both clustering data and choosing a mixture model are proposed. First, the unknown clusters are assessed. Then, the likelihood conditional to these clusters is written as the product of likelihoods from each cluster. AIC and BIC type-approximations are then applied, and the resulting criteria turn out to be the sum of the AIC or BIC relative to each cluster. The performances of our methods are evaluated on real data examples and numerical simulations
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Finite mixture models are being increasingly used to model the distributions of a wide variety of ra...
In this dissertation, we propose several methodology in clustering and mixture modeling when the use...
Two methods for both clustering data and choosing a mixture model are proposed. First, the unknown c...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this a...
This paper examines the relative performance of two commonly used clustering methods based on maximu...
Clustering is a common and important issue, and finite mixture models based on the normal distributi...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
One of the key questions in the use of mixture models concerns the choice of the number of component...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
In cluster analysis, it can be useful to interpret the partition built from the data in the light of...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Finite mixture models are being increasingly used to model the distributions of a wide variety of ra...
In this dissertation, we propose several methodology in clustering and mixture modeling when the use...
Two methods for both clustering data and choosing a mixture model are proposed. First, the unknown c...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this a...
This paper examines the relative performance of two commonly used clustering methods based on maximu...
Clustering is a common and important issue, and finite mixture models based on the normal distributi...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
One of the key questions in the use of mixture models concerns the choice of the number of component...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
In cluster analysis, it can be useful to interpret the partition built from the data in the light of...
Selecting an optimal clustering solution is a longstanding problem. In model-based clustering this ...
Finite mixture models are being increasingly used to model the distributions of a wide variety of ra...
In this dissertation, we propose several methodology in clustering and mixture modeling when the use...