衰减窗口中的不确定数据流聚类算法.

In view of the fact that uncertain data stream had the characteristics of non-convex distribution and contained a lot of noise, this paper proposed an algorithm Clu_Ustream for clustering uncertain data stream which solved the problem of real time and efficient clustering evolution for recent data. Firstly, in the online part, Clu_Ustream used the sub window sampling mechanism to collect the uncertain stream data in the sliding window. Moreover, it used a double-layer summary statistical structure linked list to store the statistical information of the probability density grids to improve the processing efficiency. Secondly, in the off-line part, it used the damped window mechanism to weaken the influence of old data and deleted regularly the expired sub windows to ensure the effectiveness of clustering. In addition, it developed a dynamic abnormal grids deletion mechanism to filter most of outliers in order to dramatically improve the space and time efficiency. The experimental results on the synthetic and real datasets show that Clu_Ustream has superior clustering quality and efficiency than other similar algorithms. [ABSTRACT FROM AUTHOR]