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