Discovering dynamics using bayesian clustering

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

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

14 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages199-209
Number of pages11
Volume1642
ISBN (Print)3540663320, 9783540663324
DOIs
StatePublished - 1999
Externally publishedYes
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Clustering
Heuristic Search
Search Strategy
Dynamic Process
Bayesian Methods
Probable
Markov processes
Military
Dynamic models
Markov chain
Entropy
Scenarios
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Sebastiani, P., Ramoni, M., Cohen, P. R., Warwick, J., & Davis, J. (1999). Discovering dynamics using bayesian clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1642, 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

Discovering dynamics using bayesian clustering. / Sebastiani, Paola; Ramoni, Marco; Cohen, Paul R; Warwick, John; Davis, James.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642 Springer Verlag, 1999. p. 199-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1642).

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

Sebastiani, P, Ramoni, M, Cohen, PR, Warwick, J & Davis, J 1999, Discovering dynamics using bayesian clustering. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1642, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1642, Springer Verlag, pp. 199-209, 3rd International Symposium on Intelligent Data Analysis, IDA 1999, Amsterdam, Netherlands, 8/9/99. https://doi.org/10.1007/3-540-48412-4_17
Sebastiani P, Ramoni M, Cohen PR, Warwick J, Davis J. Discovering dynamics using bayesian clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642. Springer Verlag. 1999. p. 199-209. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-48412-4_17
Sebastiani, Paola ; Ramoni, Marco ; Cohen, Paul R ; Warwick, John ; Davis, James. / Discovering dynamics using bayesian clustering. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642 Springer Verlag, 1999. pp. 199-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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