|
 |
| Research interests |
Machine
Learning
- Data mining
- Unsupervised learning
- Clustering
- Self-organizing maps (SOM)
- Kernel methods
- Sequential data
Human and Animal Behavior
- Social behavior
- Complex structure and dynamic of social groups
- Spatio-temporal behavior
- Individual decision-making and collective intelligence
- Self-organisation, emergent behaviour
- Learning behavior
| PhD |
Under supervision of Younès
Bennani (LIPN,
Apprentissage Artificiel
& Applications).
Subject
: "Two-level
Unsupervised
Clustering
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.