Clustering in a Multi-Agent Data Mining Environment

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.