An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes

This paper describes an unsupervised algorithm for segmenting categorical time series. The 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 segments text into words successfully, and has also been tested with a data set of mobile robot activities. We claim that the algorithm finds meaningful episodes in categorical time series, because it exploits two statistical characteristics of meaningful episodes.

• Type: Conference
• Year: 2001
• Where: IDA
• Authors: Paul R. Cohen, Niall M. Adams
• Keywords: Algorithm, Categorical Time Series, IDA 2001, Information Management, Unsupervised Algorithm