A clustering algorithm based on matrix over high dimensional data stream

Clustering high-dimensional data stream is a difficult and important issue. In this paper, we propose MStream, a new clustering algorithm based on matrix over high dimensional data stream. MStream algorithm incorporates a synopsis structure, called GC (Grid Cell Structure), and grid matrix technique. The algorithm adopts the two-phased framework. In the online component, the GC is employed to monitor one-dimensional statistics data distribution of each dimension independently. Sparse GCs which need to be deleted are checked by predefined threshold. In the offline component, it is possible to tracing multi-dimensional clusters by dense GCs which are maintained in the online component. Grid matrix technique is introduced to generate the final multi-dimensional clusters in the whole data space. Experimental results show that our algorithm has the flexible scalability and higher clustering quality.