Unsupervised Segmentation of Categorical Time Series into Episodes

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.

• Type: Conference
• Year: 2002
• Where: ICDM
• Authors: Paul R. Cohen, Brent Heeringa, Niall M. Adams
• Keywords: Algorithm, Categorical Time Series, Data Mining, ICDM 2002, Meaningful Episodes