Analyse du phénomène de la «sériation» à partir d'un tableau d'incidence
Analyse non symétrique de données par l'implication statistique
Analysis of correlation based dimension reduction methods
Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method....
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Application of discriminant, classification tree and neural network analysis to differentiate between potential glaucoma suspects with and without visual field defects.
Applications of the theory of partially ordered sets to cluster analysis
Approximation des dissimilarités par des arbres additifs
Approximation d'une préordonnance par une partition
Blokové logické schéma automatického optimalizátoru
Bounds on margin distributions in learning problems
Building the library of RNA 3D nucleotide conformations using the clustering approach
An increasing number of known RNA 3D structures contributes to the recognition of various RNA families and identification of their features. These tasks are based on an analysis of RNA conformations conducted at different levels of detail. On the other hand, the knowledge of native nucleotide conformations is crucial for structure prediction and understanding of RNA folding. However, this knowledge is stored in structural databases in a rather distributed form. Therefore, only automated methods...
Caractérisation, construction et dénombrement des ultramétriques supérieures minimales
Center-based l₁-clustering method
In this paper, we consider the l₁-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the...
Characterization of lung tumor subtypes through gene expression cluster validity assessment
The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate the significance of subclasses of diseases detectable at bio-molecular level, and more in general to support bio-medical discovery of patterns in gene expression data. In this paper we present an experimental analysis of the reliability of clusters discovered in lung tumor patients using DNA microarray data. In particular we investigate if subclasses of lung adenocarcinoma...
Classification croisée et modèles
Classification croisée et modèles
The relations between automatic clustering methods and inferentiel statistical models have mostely been studied when the data involves only one set. We propose to study these relations in the case of data involving two sets. We shall look at cross clustering methods as suggested by Govaert [6]; we show that these methods, like the simple clustering methods, can be considered as a clustering approach of a mixture model. We introduce the notion of crossed mixture from a concret example and...
Classification de données qualitatives et modèles
Classification et modèles
Classification hiérarchique à partir de matrices carrées non symétriques
Classification hiérarchique de structures mathématiques