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IJCAI
2003

Distributed Clustering Based on Sampling Local Density Estimates

8 years 10 months ago
Distributed Clustering Based on Sampling Local Density Estimates
Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.
Matthias Klusch, Stefano Lodi, Gianluca Moro
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Matthias Klusch, Stefano Lodi, Gianluca Moro
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