An Approach for Detecting Local Outliers in Grid Queries

Abstract

The density local outlier factor algorithm (LOF) needs to calculate the distance matrix for k-nearest neighbor search. The algorithm has high time complexity and is not suitable for the detection of large-scale data sets. A local outlier detection algorithm is proposed based on grid query (LOGD). In the algorithm, the k other data points closest to the data point in the target grid must be in the target grid or in the nearest neighboring grid of the target grid, it is used to improve the neighborhood query operation of the LOF algorithm, the calculation amount of the LOF algorithm is reduced in the neighborhood query. Experimental results show that the proposed LODG algorithm can effectively reduce the time of outlier detection under the condition, the detection accuracy of the original LOF algorithm is basically the same.