Determining the number of clusters in a dataset is fundamental issue data clustering. Many methods have been proposed to solve problem selecting clusters, considering it be with regard model selection. This paper proposes an efficient algorithm that automatically selects suitable based on probability distribution framework. The includes following two components. First, generalization Kohonen's ...

Ce papier présente un modèle génératif et son estimation permettant la visualisation de données binaires. Notre approche est basée sur un modèle de mélange de lois de Bernoulli par blocs et les cartes de Kohonen probabilistes. La méthode obtenue se montre à la fois parcimonieuse et pertinente en pratique.

It has been proved that in one-dimensional cases, the weights of Kohonen’s self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difŽcult to analyze Kohonen’s SOMs in multidimensional cases; however, it has been conjectured that similar results seem to be obtainable in multidimensional cases. In t...

Clustering algorithm for the moving or trajectory data provides new and helpful information. It has wide application on various location aware services. In this study the Self Organizing Map is used to form the cluster on trajectory data. The self-organizing map (SOM) is an important tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular gri...

This work is focused on the design and evaluation of a suboptimal classifier for dysarthria assessment. The classification relied on self organizing maps to discriminate 8 types of dysarthria and a normal group. The classification technique provided an excellent accuracy for assessment and enabled clinicians with a powerful relevance analysis of the input features. This technique also allows a ...