Fast Outlier Detection Algorithm in Data Stream with Local Density of Vector Dot Product
摘要
Existing outlier detection algorithms are generally time-consuming to deal with massive high-dimensional data streams.To address the problem,this paper proposes a Fast outlier detection algorithm in data stream with Local Density of Vector dot Product(FASTLDVP).It carries out incremental calculation only for the affected data points in the window,and keeps a small amount of intermediate results.Meanwhile,two optimization strategies and one pruning rule are designed to reduce the number of distance calculation times and the space-time overhead of the algorithm,so as to improve the detection efficiency.Theoretical analysis and experimental results show that this algorithm can effectively improve the detection efficiency of outliers in data stream while ensuring the detection accuracy,and can be extended to parallel environments for parallel acceleration.