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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.
 
KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

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.

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