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Mining RFID Behavior Data using Unsupervised Learning

Author

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  • Guénaël Cabanes

    (LIPN-CNRS UMR 7030, France)

  • Younès Bennani

    (LIPN-CNRS UMR 7030, France)

  • Dominique Fresneau

    (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.

Suggested Citation

  • Guénaël Cabanes & Younès Bennani & Dominique Fresneau, 2010. "Mining RFID Behavior Data using Unsupervised Learning," International Journal of Applied Logistics (IJAL), IGI Global, vol. 1(1), pages 28-47, January.
    • Handle: RePEc:igg:jal000:v:1:y:2010:i:1:p:28-47
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    Cited by:

    1. Bag, Surajit & Yadav, Gunjan & Wood, Lincoln C. & Dhamija, Pavitra & Joshi, Sudhanshu, 2020. "Industry 4.0 and the circular economy: Resource melioration in logistics," Resources Policy, Elsevier, vol. 68(C).

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