The article compares different methods for finding outliers in data sets. By using ROC and AUC curves, the researchers found that the iForest algorithm was the most effective at detecting outliers compared to other methods like Mahalanobis and k-means. They also tested the methods on real data and found that removing outliers improved the accuracy of predictive models.