@inproceedings{dca4779745da470a91972b75ae91db8e,
title = "An unsupervised algorithm for segmenting categorical timeseries into episodes",
abstract = "This paper describes an unsupervised algorithm for segmentingcateg orical 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.",
author = "Paul Cohen and Brent Heeringa and Adams, {Niall M.}",
note = "Publisher Copyright: {\textcopyright} 2002 Springer-Verlag Berlin Heidelberg.; ESF Exploratory Workshop on Pattern Detection and Discovery, 2002 ; Conference date: 16-09-2002 Through 19-09-2002",
year = "2002",
doi = "10.1007/3-540-45728-3_5",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "49--62",
editor = "Hand, {David J.} and Adams, {Niall M.} and Bolton, {Richard J.}",
booktitle = "Pattern Detection and Discovery - ESF Exploratory Workshop, Proceedings",
}