Mining RFID Behavior Data using Unsupervised Learning

Guénaël Cabanes, Younès Bennani and Dominique Fresneau
Additional contact information
Guénaël Cabanes: LIPN-CNRS UMR 7030, France
Younès Bennani: LIPN-CNRS UMR 7030, France
Dominique Fresneau: LEEC, France

International Journal of Applied Logistics (IJAL), 2010, vol. 1, issue 1, 28-47

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.

Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... .4018/jal.2010090203 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jal000:v:1:y:2010:i:1:p:28-47

Access Statistics for this article

International Journal of Applied Logistics (IJAL) is currently edited by Lincoln C. Wood

More articles in International Journal of Applied Logistics (IJAL) from IGI Global
Bibliographic data for series maintained by Journal Editor ().