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Satrap: Data and Network Heterogeneity Aware P2P Data-Mining.
- Source :
- Advances in Knowledge Discovery & Data Mining: 14th Pacific-Asia Conference, Pakdd 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II; 2010, p63-70, 8p
- Publication Year :
- 2010
-
Abstract
- Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost. A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification. However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peersĪ data which can help reduce redundant communication. In this paper, we examine the properties of network and data heterogeneity and propose a simple yet efficient P2P classification approach that minimizes expensive inter-region communication while achieving good generalization performance. Experimental results demonstrate the feasibility and effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783642136719
- Database :
- Complementary Index
- Journal :
- Advances in Knowledge Discovery & Data Mining: 14th Pacific-Asia Conference, Pakdd 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II
- Publication Type :
- Book
- Accession number :
- 76849376
- Full Text :
- https://doi.org/10.1007/978-3-642-13672-6_7