Research on clustering algoriths of data streams

This paper improved the density-based clustering algorithm of data streams and proposed Double Detection Time Strategy The strategy maintained and deleted clusters dynamically. In addition, it preserved potential outlier points with the purpose of high cluster quality and efficiency. Theory and practice show that the improved algorithm possesses good practicality and effectiveness and achieves a higher quality of clustering.