A Framework for High-Quality Clustering Uncertain Data Stream over Sliding Windows

摘要: In recent years, data mining over uncertain stream has attracted a lot of attentions along with the imprecise widely generated. many cases, estimated error is available. The very useful for clustering process, since it can be used to improve quality cluster results. this paper, we try resolve problem sliding windows. tuple expected value and uncertainty are considered meanwhile in process. We therefore propose algorithm based on Voronoi diagram reduce number distance calculation Finally, our performance study both real synthetic sets demonstrates efficiency effectiveness proposed method.