Table of Contents
Advances in Artificial Intelligence
Volume 2010, Article ID 832542, 12 pages
Research Article

Unsupervised Topographic Learning for Spatiotemporal Data Mining

LIPN-CNRS, UMR 7030, Université de Paris 13. 99, avenue J-B. Clément, 93430 Villetaneuse, France

Received 14 June 2010; Revised 5 September 2010; Accepted 7 September 2010

Academic Editor: Abbes Amira

Copyright © 2010 Guénaël Cabanes and Younès Bennani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.