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This paper presents a novel high speed clustering scheme for high dimensionaldata streams. Data stream clustering has gained importance in differentapplications, for example, in network monitoring, intrusion detection, andreal-time sensing are few of those. High dimensional stream data is inherentlymore complex when used for clustering because the evolving nature of the streamdata and high dimensionality make it non-trivial. In order to tackle thisproblem, projected subspace within the high dimensions and limited window sizeddata per unit of time are used for clustering purpose. We propose a High Speedand Dimensions data stream clustering scheme (HSDStream) which employsexponential moving averages to reduce the size of the memory and speed up theprocessing of projected subspace data stream. The proposed algorithm has beentested against HDDStream for cluster purity, memory usage, and the clustersensitivity. Experimental results have been obtained for corrected KDDintrusion detection dataset. These results show that HSDStream outperforms theHDDStream in all performance metrics, especially the memory usage and theprocessing speed.

Scaling up for high dimensional and high speed data streams: HSDStream