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
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
Visit the MMIS website
List all Multimedia and Information Systems Projects
Check out these Hot Multimedia and Information Systems Technologies:
List all Multimedia and Information Systems Technologies
