Bayesian Clustering by Dynamics

被引：1

作者：

Marco Ramoni

Paola Sebastiani

Paul Cohen

机构：

[1] Harvard Medical School,Children's Hospital Informatics Program

[2] University of Massachusetts,Department of Mathematics and Statistics

[3] University of Massachusetts,Department of Computer Science

来源：

Machine Learning | 2002年 / 47卷

关键词：

Bayesian learning; clustering; time series; Markov chains; heuristic search; entropy;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：91 / 121

页数：30

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