When the new data points are added, it can rapidly detect outliers. Since the grid number is less than data point number, the time complexity is obviously reduced under acceptable error. To solve this problem, the algorithm divides the data space into grids, and calculates the local outlier factor based on the centroids of the grids. In the static data set environment, the traditional LOF algorithm calculates the local outlier factor through the whole data set and requires a lot of computing time. Outlier detection algorithm based on deviation characteristicīy Yong Wang, Hongbin Wang, Pengcheng Sun, Xinliang YinĪbstract: Outlier mining focuses on researching rare events through detection and analysis to dig out the valuable knowledge from them.
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