Discovering dynamics using bayesian clustering

Paola Sebastiani, Marco Ramoni, Paul Cohen, John Warwick, James Davis

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

    16 Scopus citations

    Abstract

    This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy.

    Original languageEnglish (US)
    Title of host publicationAdvances in Intelligent Data Analysis - 3rd International Symposium, IDA 1999, Proceedings
    EditorsDavid J. Hand, Joost N. Kok, Michael R. Berthold
    PublisherSpringer-Verlag
    Pages199-209
    Number of pages11
    ISBN (Print)3540663320, 9783540663324
    DOIs
    StatePublished - 1999
    Event3rd International Symposium on Intelligent Data Analysis, IDA 1999 - Amsterdam, Netherlands
    Duration: Aug 9 1999Aug 11 1999

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume1642
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other3rd International Symposium on Intelligent Data Analysis, IDA 1999
    Country/TerritoryNetherlands
    CityAmsterdam
    Period8/9/998/11/99

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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