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ICDE
2008
IEEE

Approximate Clustering on Distributed Data Streams

15 years 26 days ago
Approximate Clustering on Distributed Data Streams
Abstract-- We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites which communicate through a routing tree. Distributed clustering on high speed data streams is a challenging task due to limited communication capacity, storage space, and computing power at each site. In this paper, we propose a suite of algorithms for computing (1 + )-approximate k-median clustering over distributed data streams under three different topology settings: topologyoblivious, height-aware, and path-aware. Our algorithms reduce the maximum per node transmission to polylog N (opposed to (N) for transmitting the raw data). We have simulated our algorithms on a distributed stream system with both real and synthetic datasets composed of millions of data. In practice, our algorithms are able to reduce the data transmission to a small fraction of the original data. Moreover, our results indicate that the ...
Qi Zhang, Jinze Liu, Wei Wang 0010
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2008
Where ICDE
Authors Qi Zhang, Jinze Liu, Wei Wang 0010
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