Abstract: Based on local information,a new outlier detection algorithm was designed to calculate density-based uncertain local outlier factor (ULOF) for each point in an uncertain dataset.Firstly,by establishing the possible world model,we calculated the probability of possible word for uncertain data.Then we combined the traditional LOF algorithm to derivate the ULOF algorithm formula,and judged the degree outlier of each data according to the ULOF value.We also did a detailed analysis for efficiency and accuracy of ULOF algorithm.At the same time,we proposed gird-based pruning strategy and k-nearest neighborhood query optimization to reduce the candidate dataset.At last the results of several experiments on synthetic data demonstrate the feasibility and effectiveness of the proposed approach.Optimized NLOF algorithm can improve the outlier detection accuracy,reduce the time complexity and improve the performance of outlier detection on uncertain data.

Key words: Uncertain data,Local outlier detection,Possible world model,k-nearest neighborhood