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In recent years, the size and complexity of datasetshave shown an exponential growth. In many application areas,huge amounts of data are generated, explicitly or implicitlycontaining spatial or spatiotemporal information. However, theability to analyze these data remains inadequate, and the needfor adapted data mining tools becomes a major challenge. Inthis paper, we propose a new unsupervised algorithm, suitablefor the analysis of noisy spatiotemporal Radio FrequencyIDentification (RFID) data. Two real applications show thatthis algorithm is an efficient data-mining tool for behavioralstudies based on RFID technology. It allows discovering andcomparing stable patterns in an RFID signal and is suitable forcontinuous learning

Unsupervised Topographic Learning for Spatiotemporal Data Mining