driven by neighborhood and density".
Abstract: The research outlined in this thesis concerns the development of approaches based on Self-Organizing Map (SOM) for the discovery and the monitoring of class structures in the data through unsupervised learning. We propose a simultaneously two levels clustering method. This method is based on the estimate, from the data, of connectivity and density values of the SOM's prototypes. The number of clusters is detected automatically. Moreover, the complexity is linear with the number of data. We show that it is relatively simple and efficient to adapt these algorithms to variants of the SOM in order to obtain a versatile method capable of analyzing different data types. We also propose an improvement of the quality of the SOM using the connectivity values during the learning of the prototypes. We describe a new method of condensed description of the data distribution and a heuristic measure of similarity between these models. These algorithms are based on an estimate of the underlying density for learning a modified SOM. In addition, we combine the clustering algorithm to measure similarity between distributions for the analysis of evolutionary data, and we propose an algorithm for monitoring data stream. Finally, we present two applications for tracking individuals in a RFID device. The first application is a study of the behavior of a colony of ants while moving. The second application require tracking of customers in a store.