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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 91-121 |
| Number of pages | 31 |
| Journal | Machine Learning |
| Volume | 47 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 1 2002 |
Keywords
- Bayesian learning
- Clustering
- Entropy
- Heuristic search
- Markov chains
- Time series
ASJC Scopus subject areas
- Software
- Artificial Intelligence
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Cite this
Ramoni, M., Sebastiani, P., & Cohen, P. (2002). Bayesian clustering by dynamics. Machine Learning, 47(1), 91-121. https://doi.org/10.1023/A:1013635829250