Discovering dynamics using bayesian clustering

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

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

14 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
CountryNetherlands
CityAmsterdam
Period8/9/998/11/99

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Sebastiani, P., Ramoni, M., Cohen, P., Warwick, J., & Davis, J. (1999). Discovering dynamics using bayesian clustering. In D. J. Hand, J. N. Kok, & M. R. Berthold (Eds.), Advances in Intelligent Data Analysis - 3rd International Symposium, IDA 1999, Proceedings (pp. 199-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1642). Springer-Verlag. https://doi.org/10.1007/3-540-48412-4_17