Clustering in a multi-agent data mining environment

Authors:
Santhana Chaimontree;Katie Atkinson;Frans Coenen
Affiliations:
Department of Computer Science, University of Liverpool, UK;Department of Computer Science, University of Liverpool, UK;Department of Computer Science, University of Liverpool, UK
Venue:
ADMI'10 Proceedings of the 6th international conference on Agents and data mining interaction
Year:
2010

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Abstract

A Multi-Agent based approach to clustering using a generic Multi-Agent Data Mining (MADM) framework is described. The process use a collection of agents, running several different clustering algorithms, to determine a "best" cluster configuration. The issue of determining the most appropriate configuration is a challenging one, and is addressed in this paper by considering two metrics, total Within Group Average Distance (WGAD) to determine cluster cohesion, and total Between Group Distance (BGD) to determine separation. The proposed process is implemented using the MASminer MADM framework which is also introduced in this paper. Both the clustering technique and MASminer are evaluated. Comparison of the two "best fit" measures indicates that WGAD can be argued to be the most appropriate metric.