Clustering algorithm for probabilistic data stream over sliding windows

An effective clustering algorithm called PWStream for probabilistic data stream over sliding window is developed in this paper. The algorithm uses exponential histogram of cluster feature to store the summary information of the most recently arrived tuples, and outdated information is deleted within a certain guaranteed range of error. For the uncertain tuples in data stream, the concepts of strong cluster, transitional cluster and weak cluster are proposed in the PWStream. With these concepts, an effective strategy of choosing cluster based on distance and existence probability is designed, which can find more strong clusters. Theoretical analysis and comprehensive experimental results demonstrate that the proposed method is of high quality and fast processing rate.