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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
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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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Future Internet is...


Future Internet
With over a billion users, today's Internet is arguably the most successful human artifact ever created. The Internet's physical infrastructure, software, and content now play an integral part of the lives of everyone on the planet, whether they interact with it directly or not. Now nearing its fifth decade, the Internet has shown remarkable resilience and flexibility in the face of ever increasing numbers of users, data volume, and changing usage patterns, but faces growing challenges in meetings the needs of our knowledge society. Globally, many major initiatives are underway to address the need for more scientific research, physical infrastructure investment, better education, and better utilisation of the Internet. Within Japan, USA and Europe major new initiatives have begun in the area.

To succeed the Future Internet will need to address a number of cross-cutting challenges including:

  • Scalability in the face of peer-to-peer traffic, decentralisation, and increased openness

  • Trust when government, medical, financial, personal data are increasingly trusted to the cloud, and middleware will increasingly use dynamic service selection

  • Interoperability of semantic data and metadata, and of services which will be dynamically orchestrated

  • Pervasive usability for users of mobile devices, different languages, cultures and physical abilities

  • Mobility for users who expect a seamless experience across spaces, devices, and velocities