Affiliation:
1. LIPN-CNRS UMR 7030, France
2. LEEC, France
Abstract
Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density-based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.
Reference32 articles.
1. Aupetit, M. (2005). Learning topology with the generative gaussian graph and the EM algorithm. In Neural Information Processing Systems (NIPS). Vancouver, B.C., Canada.
2. Bohez, E. L. J. (1998). Two level cluster analysis based on fractal dimension and iterated function systems (ifs) for speech signal recognition. IEEE Asia-Pacific Conference on Circuits and Systems (pp. 291–294).
3. The Giant Nests of the African Stink Ant Paltothyreus tarsatus (Formicidae, Ponerinae)
4. Cabanes, G., & Bennani, Y. (2007). A simultaneous two-level clustering algorithm for automatic model selection. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA’07) (pp. 1176–1182), Cincinnati, Ohio, USA.
5. Cabanes, G., & Bennani, Y. (2008). A local density-based simultaneous two-level algorithm for topographic clustering. In International Joint Conference on Neural Network (IJCNN), Hong-Kong, China.