Approximate Clustering on Distributed Data Streams

Qi Zhang, Jinze Liu, Wei Wang
2008 2008 IEEE 24th International Conference on Data Engineering  
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
more » ... under three different topology settings: topologyoblivious, height-aware, and path-aware. Our algorithms reduce the maximum per node transmission to polylog N (opposed to Q(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 algorithms are scalable with respect to the data volume, approximation factor, and the number of sites.
doi:10.1109/icde.2008.4497522 dblp:conf/icde/ZhangLW08 fatcat:uc4sefkn2jg4fim7lktluirtk4