An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes
作者： Paul Cohen , Brent Heeringa , Niall M. Adams
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摘要: This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The Voting-Experts first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over has two "expert methods" decide where in boundaries should be drawn. successfully segments text words four languages. also robot sensor data subsequences that represent episodes life robot. We claim VOTING-EXPERTS finds meaningful because it exploits statistical characteristics
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