Machine Learning

, Volume 47, Issue 1, pp 91–121

Bayesian Clustering by Dynamics

  • Marco Ramoni
  • Paola Sebastiani
  • Paul Cohen

DOI: 10.1023/A:1013635829250

Cite this article as:
Ramoni, M., Sebastiani, P. & Cohen, P. Machine Learning (2002) 47: 91. doi:10.1023/A:1013635829250


This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy. A controlled experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions and, when applied to clustering sensor data from mobile robots, it produces clusters that are meaningful in the domain of application.

Bayesian learning clustering time series Markov chains heuristic search entropy 

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Marco Ramoni
    • 1
  • Paola Sebastiani
    • 2
  • Paul Cohen
    • 3
  1. 1.Children's Hospital Informatics ProgramHarvard Medical SchoolBostonUSA
  2. 2.Department of Mathematics and StatisticsUniversity of MassachusettsAmherstUSA
  3. 3.Department of Computer ScienceUniversity of MassachusettsAmherstUSA

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