ML Anthology

Bayesian Clustering by Dynamics

Marco Ramoni, Paola Sebastiani, Paul R. Cohen
MLJ 2002 pp. 91-121
doi:10.1023/A:1013635829250 /mlj/2002/ramoni2002mlj-bayesian/

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.

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Text

Ramoni et al. "Bayesian Clustering by Dynamics." Machine Learning, 2002. doi:10.1023/A:1013635829250

Markdown

[Ramoni et al. "Bayesian Clustering by Dynamics." Machine Learning, 2002.](https://mlanthology.org/mlj/2002/ramoni2002mlj-bayesian/) doi:10.1023/A:1013635829250

BibTeX

@article{ramoni2002mlj-bayesian,
  title     = {{Bayesian Clustering by Dynamics}},
  author    = {Ramoni, Marco and Sebastiani, Paola and Cohen, Paul R.},
  journal   = {Machine Learning},
  year      = {2002},
  pages     = {91-121},
  doi       = {10.1023/A:1013635829250},
  volume    = {47},
  url       = {https://mlanthology.org/mlj/2002/ramoni2002mlj-bayesian/}
}