T-Patterns revisited: mining for temporal patterns in sensor data

Sensors (Basel). 2010;10(8):7496-513. doi: 10.3390/s100807496. Epub 2010 Aug 10.


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.

Keywords: Gaussian mixture model; Lempel-Ziv; MERL motion data; T-patterns; sensor networks; temporal pattern extraction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Data Mining*
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Software