Abstract

The increasing demands to scale up to massive data sets inherently distributed over a network with limited band width and computational resources available motivated the development of distributed data mining (DDM). Multi-agent systems offer architecture for distributed problem solving. Distributed data mining algorithms specialize on one class of such distributed problem solving tasks—analysis and modelling of distributed data. This paper offers a perspective on distributed data mining algorithms in the context of multi-agents systems. It particularly focuses on distributed clustering algorithms and their potential applications in multi-agent-based problem solving scenarios. A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has-been generated, the agents are able to negotiate with a view to improving on this initial clustering

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