A Generic and Extendible Multi-Agent Data Mining Framework

Authors:
Kamal Ali Albashiri;Frans Coenen
Affiliations:
Department of Computer Science, The University of Liverpool, Liverpool, United Kingdom L69 3BX;Department of Computer Science, The University of Liverpool, Liverpool, United Kingdom L69 3BX
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

A generic and extendible Multi-Agent Data Mining (MADM) framework, EMADS (the Extendible Multi-Agent Data mining System) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a system of wrappers. The advantage offered is that the system is easily extendible, so that further data agents and mining agents can simply be added to the system. A demonstration EMADS framework is currently available. The paper includes details of the EMADS architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework's operation is provided by considering two MADM scenarios.