From market baskets to mole rats: using data mining techniques to analyze RFID data describing laboratory animal behavior

Authors:
Daniel P. McCloskey;Michael E. Kress;Susan P. Imberman;Igor Kushnir;Susan Briffa-Mirabella
Affiliations:
College of Staten Island, City University of New York, Staten Island, NY, USA;College of Staten Island, City University of New York, Staten Island, NY, USA;College of Staten Island, City University of New York, Staten Island, NY, USA;College of Staten Island, City University of New York, Staten Island, NY, USA;College of Staten Island, City University of New York, Staten Island, NY, USA
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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Abstract

The use of new technologies, such as RFID sensors, provides scientists with novel ways of doing experimental research. As scientists become more technologically savvy and use these techniques, the traditional approaches to data analysis fail given the huge amounts of data produced by these methods. In this paper we describe an experiment in which colonies of naked mole rats were tagged with RFID transponders. RFID sensors were strategically placed in the mole rat caging system. The goal of this experiment was to document and analyze the interactions between animals. The huge amount of data produced by the sensors was not analyzable using the traditional methods employed by behavioral neuroscience researchers. Computational methods used by data miners, such as cluster analysis, association rule mining, and graphical models, were able to scale to the data and produce knowledge and insight that was previously unknown. This paper describes in detail the experimental setup and the computational methods used.