K-nearest neighbor search in peer-to-peer systems

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Mashayekhi, H ; Sharif University of Technology

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Type of Document: Article
Abstract:
Data classification in large scale systems, such as peer-to-peer networks, can be very communication-expensive and impractical due to the huge amount of available data and lack of central control. Frequent data updates pose even more difficulties when applying existing classification techniques in peer-to-peer networks. We propose a distributed, scalable and robust classification algorithm based on k-nearest neighbor estimation. Our algorithm is asynchronous, considers data updates and imposes low communication overhead. The proposed method uses a content based overlay structure to organize data and moderate the number of query messages propagated in the network. Simulation results show that our algorithm performs efficiently in large scale networks
Keywords:
Classification ; Content addressable network ; K-nearest neighbors ; Peer-to-peer systems ; Classification technique ; Communication overheads ; Content-addressable networks ; K nearest neighbor search ; K-nearest neighbor estimation ; Peer-to-Peer system ; Robust classification ; Algorithms ; Classification (of information) ; Communication ; Membership functions ; Peer to peer networks ; Text processing ; Distributed computer systems
Source: AP2PS 2010 - 2nd International Conference on Advances in P2P Systems ; 2010 , Pages 100-105 ; 9781612081021 (ISBN)