Tech Report

Sequence Learning via Bayesian Clustering by Dynamics

This chapter 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 algorithm uses an entropy-based heuristic search strategy. When applied to clustering sensor data from mobile robots, the algorithm produces clusters that are meaningful in the domains of application.

1. Department of Mathematics, Imperial College of Science, Technology and Medicine

2. Knowledge Media Institute, The Open University

3. Department of Computer Science, University of Massachusetts at Amherst

Publication(s)

Also in Sequence Learning: Paradigms, Algorithms, and Applications, L. Giles and R. Sun, editors, Springer, New York, NY, 2000.

ID: kmi-00-05

Date: 2000

Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen

Resources:
Download PDF

View By

Other Publications

Latest Seminar
Dr Nirwan Sharma
Knowledge Media Institute, The Open University

Designing Dialogic Human-AI interfaces to further Citizen Science practice.

Watch the live webcast

CONTACT US

Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support

COMMENT

If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team