Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9650515 | Engineering Applications of Artificial Intelligence | 2005 | 17 Pages |
Abstract
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 multi-agents 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 privacy-preserving 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Josenildo C. da Silva, Chris Giannella, Ruchita Bhargava, Hillol Kargupta, Matthias Klusch,