Location of Repository

c ○ 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Bayesian Clustering by Dynamics

By Marco Ramoni, Paola Sebastiani, Paul Cohen and H Fisher


Abstract. 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. Keywords

Topics: Bayesian learning, clustering, time series, Markov chains, heuristic search
Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.isi.edu/div3/div3/p... (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.