Density-based top-k outlier detection on uncertain objects

Outlier detection is an important task in data mining and has been well studied on precise data. However, outlier detection on uncertain objects is particularly challenging. In this paper, firstly, the conceptions about density-based top-k uncertain outlier detection are defined. Secondly, an algorithm of density-based Top-k outlier detection on uncertain objects is proposed, the time complexity of which is polynomial. Finally, the experiment illustrates the effectiveness and efficiency of the algorithm.