Unsupervised Segmentation of Categorical Time Series into Episodes

Authors:
Paul Cohen;Brent Heeringa;Niall Adams
Affiliations:
-;-;-
Venue:
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Year:
2002

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Abstract

This paper describes an unsupervised algorithm forsegmenting categorical time series into episodes. TheVOTING-EXPERTS algorithm first collects statistics aboutthe frequency and boundary entropy of ngrams, then passesa window over the series and has two "expert methods" decidewhere in the window boundaries should be drawn. Thealgorithm successfully segments text into words in four languages.The algorithm also segments time series of robotsensor data into subsequences that represent episodes inthe life of the robot. We claim that VOTING-EXPERTSfinds meaningful episodes in categorical time series becauseit exploits two statistical characteristics of meaningfulepisodes.