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 ...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Qi Zhang, Jinze Liu, Wei Wang 0010
• Keywords: Data Streams, Database, ICDE 2008, Speed Data Streams, Stream Data