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Article Dans Une Revue International Journal of Applied Logistics Année : 2010

Mining RFID Behavior Data using Unsupervised Learning

Résumé

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, we aim to develop new tools for automatic data mining. In this paper, we study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. We 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. Keywords: RFID; spatio-temporal data; automatic data mining; unsupervised learning; ants behavior. INTRODUCTION Radio Frequency IDentification (RFID) is an advanced tracking technology. The RFID tags, which consist of a microchip and an antenna, must be used with a reader that can detect simultaneously a lot of tags in a single scan. A computer has to be used to store the data about the position of each tag for each scan in a database. This allows different analyses. RFID systems can be used to study animal societies. Animal societies are dynamic complex systems characterized by numerous interactions between individual members. Such dynamic structures stem from the synergy of these interactions, the individual capacities in information processing and the diversity of individual responses (Fresneau et al., 1989). RFID, thanks to miniaturization, offers the advantage of automation and overcomes the constraints imposed by video analyzes. Indeed, video recording allows long-duration tracking, however the time for analyzes highly increases with the number of individuals monitored. It also imposes strong constraints (as the need of a minimum illumination and high contrast between the animals and the environment) and it does not work when the ant is moving in a reverse position which doesn't allow individual identification. The aim of this work is to develop a new RFID-based autonomous system to follow the spatio-temporal activity of groups, which is currently very difficult to study in its entirety and to develop new tools for automatic data processing. These objectives have necessarily led to an interdisciplinary project combining behavioral and complex systems sciences with computer and engineering sciences.
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Dates et versions

hal-01461442 , version 1 (08-02-2017)

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  • HAL Id : hal-01461442 , version 1

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Guénaël Cabanes, Younès Bennani, Dominique Fresneau. Mining RFID Behavior Data using Unsupervised Learning. International Journal of Applied Logistics, 2010. ⟨hal-01461442⟩
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