Multi-agent based clustering: Towards generic multi-agent data mining

Abstract

A framework for Multi Agent Data Mining (MADM) is described. The framework comprises a collection of agents cooperating to address given data mining tasks. The fundamental concept underpinning the framework is that it should support generic data mining. The vision is that of a system that grows in an organic manner. The central issue to facilitating this growth is the communication medium required to support agent interaction. This issue is partly addressed by the nature of the proposed architecture and partly through an extendable ontology; both are described. The advantages offered by the framework are illustrated in this paper by considering a clustering application. The motivation for the latter is that no "best" clustering algorithm has been identified, and consequently an agent-based approach can be adopted to identify "best" clusters. The application serves to demonstrates the full potential of MADM. © 2010 Springer-Verlag Berlin Heidelberg.