Abstract：In order to solve the problem that the existing outlier detection algorithms have low detection accuracy for irregular shape data sets and complex distributed multidimensional data sets,an outlier detection algorithm based on similarity pruning is proposed.Firstly,the similarity matrix construction method is used to calculate the similarity between sample points.A part of the sample points with smaller similarity to other samples is found as the outlier candidate set by the degree matrix.And then the LOF algorithm is used to calculate the local outlier factor of all the objects in the outlier candidate set,and the final outliers are obtained according to the numerical value of the local outlier factor.The experimental results show that the proposed algorithm can obtain high outlier detection accuracy.
2018, Vol. 39 
