Based on the flow of anti-k nearest neighbors algorithm for data mining outliers

A new data stream outlier detection algorithm SODRNN is proposed based on reverse nearest neighbors. this paper researches data stream outlier detection algorithm which is based on Reverse k nearest neighbours. When we analyze the known algorithms, we find that the algorithm cannot deal with the concept drifting problem and they need multi-scan of the dataset. So, this paper introduces the SODRNN algorithm, which needs only one pass of scan for the current sliding window. The empirical study verify the feasibility and effectiveness of X*tree index structure which supports knn searching and the SODRNN algorithm in this paper.