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Tech Report kmi-00-04 Abstract


Multivariate Clustering by Dynamics
Techreport ID: kmi-00-04
Date: 2000
Author(s): Marco Ramoni, Paola Sebastiani and Paul Cohen
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We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a time series is approximated by a first order Markov Chain and the overall joint distribution of the variables is simplified by conditional independence assumptions. The algorithm searches for the most probable set of clusters given the data using a entropy-based heuristic search method. The algorithm is evaluated on a set of multivariate time series of propositions produced by the perceptual system of a mobile robot. 1. Knowledge Media Institute, The Open University 2. Department of Mathematics, Imperial College of Science, Technology and Medicine 3. Department of Computer Science, University of Massachusetts at Amherst.

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Also in Proceedings of the 2000 National Conference on Artificial Intelligence (AAAI-2000), Morgan Kaufman, San Mateo, CA, 2000.
 
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Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

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.