Publication Date
2002
Abstract
This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The Voting-Experts algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two “expert methods” decide where in the window boundaries should be drawn. The algorithm successfully segments text into words in four languages. The algorithm also segments time series of robot sensor data into subsequences that represent episodes in the life of the robot. We claim that Voting-Experts finds meaningful episodes in categorical time series because it exploits two statistical characteristics of meaningful episodes.
Recommended Citation
Cohen, Paul; Heeringa, Brent; and Adams, Niall, "An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes" (2002). Computer Science Department Faculty Publication Series. 191.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/191
Comments
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