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Distributed Data Mining and Agents

Josenildo Costa da Silva; C. Giannella; R. Bhargava; H. Kargupta; Matthias Klusch
In: B. Grabot (Hrsg.). International Journal Engineering Applications of Artificial Intelligence, Vol. 18, No. 7, Pages 791-807, Elsevier Science Publishers B. V, 10/2005.


Multi-Agent Systems (MAS) offer an architecture for distributed problem solving. Distributed Data Mining (DDM) algorithms focus on one class of such distributed problem solving tasks - analysis and modeling of distributed data. This paper offers a perspective on DDM algorithms in the context of multiagents systems. It discusses broadly the connection between DDM and MAS. It provides a high-level survey of DDM, then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. It reviews algorithms for distributed clustering, including privacypreserving ones. It describes challenges for clustering in sensor-network environments, potential shortcomings of the current algorithms, and future work accordingly. It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering.