Bayesian clustering by dynamics

Marco Ramoni, Paola Sebastiani, Paul Cohen

Research output: Contribution to journalArticlepeer-review

127 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)91-121
Number of pages31
JournalMachine Learning
Issue number1
StatePublished - Apr 2002
Externally publishedYes


  • Bayesian learning
  • Clustering
  • Entropy
  • Heuristic search
  • Markov chains
  • Time series

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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