Outlier detection algorithm based on density and distance

Abstract

In order to solve the problem that the existing outlier detection algorithm is difficult to detect the one-dimensional integer data set with uneven frequency distribution and uniform distance distribution and low accuracy, the advantages of density outlier detection and distance outlier detection can be combined. An outlier detection algorithm DAD (Density and Distance) based on density and distance was proposed. In order to improve the possibility of outlier sample distance, the algorithm can define the weight distance; introduce the global density outlier factor combined with the weight distance as the relative distance, and use the cutting edge strategy to quickly cut the outliers based on the minimum spanning tree. Then, an artificial data set was used to test the algorithm. Experimental results showed that the algorithm had a good outlier detection accuracy in dealing with data sets with uneven frequency distribution and uniform distance distribution.