Tech Reports
Tech Report kmi-99-05 Abstract
Bayesian Clustering by Dynamics
Techreport ID: kmi-99-05
Date: 1999
Author(s): Marco Ramoni, Paola Sebastiani, Paul Cohen, John Warwick and James Davis
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. An experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions. When the method is applied to clustering simulated military engagements and sensor data from mobile robots, it produces clusters that are meaningful in the domains of application. 1. Knowledge Media Institute, The Open University 2. Department of Statistics, The Open University. 3. Department of Computer Science, University of Massachusetts at Amherst.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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