An unsupervised algorithm for segmenting categorical timeseries into episodes

Paul Cohen, Brent Heeringa, Niall M. Adams

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

    10 Scopus citations

    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.

    Original languageEnglish (US)
    Title of host publicationPattern Detection and Discovery - ESF Exploratory Workshop, Proceedings
    EditorsDavid J. Hand, Niall M. Adams, Richard J. Bolton
    PublisherSpringer-Verlag
    Pages49-62
    Number of pages14
    ISBN (Electronic)9783540441489
    DOIs
    StatePublished - 2002
    EventESF Exploratory Workshop on Pattern Detection and Discovery, 2002 - London, United Kingdom
    Duration: Sep 16 2002Sep 19 2002

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2447
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    OtherESF Exploratory Workshop on Pattern Detection and Discovery, 2002
    Country/TerritoryUnited Kingdom
    CityLondon
    Period9/16/029/19/02

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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