Weighted minimum consecutive pair of the extreme pole outlier factor

Outlier concept is one significant topics in data mining. Many researches in outlier detections propose algorithm to generate the outlier scores which can be used to measure the outlierness of an instance in a dataset. This paper proposes a new parameter-free algorithm called a weighted minimum consecutive pair of the extreme pole outlier factor (WOF). The new outlier score of an instance is generated along the extreme poles by considering the projection of this instance and its consecutive pair. The minimum on each side of the instance will be weighted and used to create the WOF. The WOF algorithm is implemented and has the O(n
²
) time complexity. It has 100% accuracy on three sets of synthetic datasets.