Distributed Data Mining and Multi Agent-Based Distributed Data Clustering

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