关键词: 不确定数据, 数据挖掘, 异常点, top-k

Abstract: In recent years, along with the appearance of uncertain data, outlier detection on uncertain data stream becomes a new hotspot. However, three parameters are contained in the existing definition of outlier on uncertain data, it is very difficult for users to set these parameters, the user cannot get the suitable outlier. Most of the time, the users would like to get the objects which are most likely to be outliers. This paper proposes the top-k outlier detection on uncertain data stream. The proposed method prunes objects based on the estimation of the range of probabilities being outlier and reduces some unnecessary computation. Meanwhile, this paper proposes the incremental method for computing the range of probabilities to improve efficiency. Finally, the performance of the proposed method is verified through a number of simulation experiments on real and synthetic datasets.

Key words: uncertain data, data mining, outlier, top-k