none2The most frequently used hierarchical methods for clustering of quantitative variables are based on bivariate or multivariate correlation measures. These solutions can be unsuitable in presence of uncorrelated but collinear variables. We propose a new measure of collinearity between groups of variables that can be used for their hierarchical clustering. Its main theoretical features are described and its performance is evaluated both on simulated and real data sets.Titolo della collana: Studies in classification, data analysis, and knowledge organizationnoneA. Laghi; G. SoffrittiA. Laghi; G. Soffritt
The clustering of objects-individuals is one of the most widely usedapproaches to exploring multidim...
The purpose of the paper is to present a new statistical approach to hierarchical cluster analysis w...
We propose a new measure to evaluate the dissimilarity between rankings in hierarchical cluster anal...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
International audienceThe clustering of objects (individuals or variables) is one of the most used a...
In this work, we begin with the presentation of the Tθ family of usual similarity measures concernin...
We assess the performance of a new clustering method for Hierarchical Factor Classification of varia...
In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for cl...
Complex multidimensional concepts are often explained by a tree-shape structure by considering neste...
3In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated...
<p>A correlation matrix together with clustering (i.e., Pearson uncentered) of the feature points is...
agglomerative hierarchical clustering methods. Agglomerative hierarchical clustering methods are bot...
Clustering of variables is as a way to arrange variables into homogeneous clusters i.e. groups of va...
The detection of correlations between different fea-tures in high dimensional data sets is a very im...
Cluster analysis is one of the classification methods of multivariate statistical analysis. The task...
The clustering of objects-individuals is one of the most widely usedapproaches to exploring multidim...
The purpose of the paper is to present a new statistical approach to hierarchical cluster analysis w...
We propose a new measure to evaluate the dissimilarity between rankings in hierarchical cluster anal...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
International audienceThe clustering of objects (individuals or variables) is one of the most used a...
In this work, we begin with the presentation of the Tθ family of usual similarity measures concernin...
We assess the performance of a new clustering method for Hierarchical Factor Classification of varia...
In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for cl...
Complex multidimensional concepts are often explained by a tree-shape structure by considering neste...
3In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated...
<p>A correlation matrix together with clustering (i.e., Pearson uncentered) of the feature points is...
agglomerative hierarchical clustering methods. Agglomerative hierarchical clustering methods are bot...
Clustering of variables is as a way to arrange variables into homogeneous clusters i.e. groups of va...
The detection of correlations between different fea-tures in high dimensional data sets is a very im...
Cluster analysis is one of the classification methods of multivariate statistical analysis. The task...
The clustering of objects-individuals is one of the most widely usedapproaches to exploring multidim...
The purpose of the paper is to present a new statistical approach to hierarchical cluster analysis w...
We propose a new measure to evaluate the dissimilarity between rankings in hierarchical cluster anal...