CDM Seminar Series 2002-03


Learning States and Rules for Time-Series Anomaly Detection
Dr. Philip Chan
Associate Professor of Computer Science
Florida Institute of Technology
Visiting Scientist - MIT LCS Clinical Decision Making Group

March 19, 2003

In this talk we investigate machine learning techniques for generating
knowledge that can monitor the operation of devices or systems.
Specifically, we study methods for generating models that can detect
anomalies from time-series data. The normal operation of a device can
usually be characterized in different temporal states. To identify
these states, we introduce a clustering algorithm called Gecko that
can automatically determine a reasonable number of clusters using our
proposed "L" method. We then use the RIPPER classification rule
learning algorithm to describe these states in logical rules.
Finally, transition logic between the states is added to create a
finite state automaton. Our empirical results, on data obtained from
the NASA shuttle program, indicate that Gecko performs comparably to a
NASA human expert in identifying states and our overall system can
track normal behavior and detect anomalies.



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