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Outlier detection (also known as anomaly detection or deviation detection) isa process of detecting data points in which their patterns deviatesignificantly from others. It is common to have outliers in industryapplications, which could be generated by different causes such as human error,fraudulent activities, or system failure. Recently, density-based methods haveshown promising results, particularly among which Local Outlier Factor (LOF) isarguably dominating. However, one of the major drawbacks of LOF is that it iscomputationally expensive. Motivated by the mentioned problem, this researchpresents a novel pruning-based procedure in which the execution time of LOF isreduced while the performance is maintained. A novel Prune-based Local OutlierFactor (PLOF) approach is proposed, in which prior to employing LOF,outlierness of each data instance is measured. Next, based on a threshold, datainstances that require further investigation are separated and LOF score isonly computed for these points. Extensive experiments have been conducted andresults are promising. Comparison experiments with the original LOF and twostate-of-the-art variants of LOF have shown that PLOF produces higher accuracyand precision while reducing execution time.

Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)