@Article{AlbertAliSalah2010,
author="Albert Ali Salah
and E. Pauwels
and R. Tavenard
and Theo Gevers",
title="T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data",
journal="Sensors",
year="2010",
volume="10",
number="8",
pages="7496--7513",
optkeywords="sensor networks",
optkeywords="temporal pattern extraction",
optkeywords="T-patterns",
optkeywords="Lempel-Ziv",
optkeywords="Gaussian mixture model",
optkeywords="MERL motion data",
abstract="The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.",
optnote="ALTRES;ISE",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1845), last updated on Tue, 12 Jul 2016 10:57:06 +0200",
doi="10.3390/s100807496"
}