TY - GEN
T1 - Unsupervised segmentation of categorical time series into episodes
AU - Cohen, Paul
AU - Heeringa, Brent
AU - Adams, Niall
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:36348933993
SN - 0769517544
SN - 9780769517544
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 99
EP - 106
BT - Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
T2 - 2nd IEEE International Conference on Data Mining, ICDM '02
Y2 - 9 December 2002 through 12 December 2002
ER -