Tech Reports
Tech Report kmi-99-04 Abstract
Discovering Dynamics using Bayesian Clustering
Techreport ID: kmi-99-04
Date: 1999
Author(s): Paola Sebastiani, Marco Ramoni, Paul Cohen, John Warwick and James Davis
This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy. 1. Department of Statistics, The Open University. 2. Knowledge Media Institute, The Open University. 3. Department of Computer Science, University of Massachusetts at Amherst.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Future Internet is...

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
Future Internet from KMi.
Check out these Hot Future Internet Projects:
List all Future Internet Projects
Check out these Hot Future Internet Technologies:
List all Future Internet Technologies
List all Future Internet Projects
Check out these Hot Future Internet Technologies:
List all Future Internet Technologies



