Unsupervised segmentation of categorical time series into episodes

Paul Cohen, Brent Heeringa, Niall Adams

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    21 Scopus citations

    Abstract

    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.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
    Pages99-106
    Number of pages8
    StatePublished - 2002
    Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
    Duration: Dec 9 2002Dec 12 2002

    Publication series

    NameProceedings - IEEE International Conference on Data Mining, ICDM
    ISSN (Print)1550-4786

    Other

    Other2nd IEEE International Conference on Data Mining, ICDM '02
    Country/TerritoryJapan
    CityMaebashi
    Period12/9/0212/12/02

    ASJC Scopus subject areas

    • Engineering(all)

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