Abstract：In the DBSCAN outlier detection algorithm based on clustering,there are problems of uncertainty and global uniformity of the parameter Eps.Therefore,this paper first proposes an adaptive DBSCAN outlier detection algorithm based on multi-objective optimization.According to the characteristics of the data set,the NSGA-II optimization algorithm is used to adaptively solve an optimal Eps for each data in the data set,which not only avoids the insufficiency of parameter setting by human experience,but also solves the problem of clustering inaccuracy caused by global parameters.Secondly,the outlier detection is performed through the LOF algorithm based on Eps,which reduces the amount of calculation.Finally,through experimental comparisons under different data sets,the results show that the algorithm proposed in this paper has a higher accuracy for detecting outliers.
2022, Vol. 43 
