摘要：In recent years, the size and complexity of datasets
have shown an exponential growth. In many application areas,
huge amounts of data are generated, explicitly or implicitly
containing spatial or spatiotemporal information. However, the
ability to analyze these data remains inadequate, and the need
for adapted data mining tools becomes a major challenge. In
this paper, we propose a new unsupervised algorithm, suitable
for the analysis of noisy spatiotemporal Radio Frequency
IDentification (RFID) data. Two real applications show that
this algorithm is an efficient data-mining tool for behavioral
studies based on RFID technology. It allows discovering and
comparing stable patterns in an RFID signal and is suitable for
continuous learning.
