Parameter-free outlier detection factor using weighted minimum consecutive pair

Abstract

Outlier concept is one of the most significant topics in data mining. Many researches in outlier detections address an algorithm to generate the outlier scores which can be used to measure the outlierness of an instance in a dataset. Ordered distance difference outlier factor (OOF) is the parameter-free outlier detection algorithm which was published in 2013. This thesis proposes a new parameter-free outlier detection 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 radial 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 has the O(n2) time complexity. To compare the effectiveness and time, WOF algorithm was applied with generated synthetic datasets and three UCI datasets.