Mass-ratio-variance based Outlier Factor

Abstract

An outlier of a finite dataset in statistics is defined as a data point that differs significantly from others. It is normally surrounded by a few data points while normal ones are engulfed by others. This behavior leads to the proposed outlier factor called Mass-ratio-variance Outlier Factor (MOF). A score is assigned to a data point from the variance of the mass-ratio distribution from the rest of data points. Within a sphere of an outlier, there will be few data points compared with a normal one. So, the mass-ratio of an outlier will be different from that of a normal data point. The algorithm to generate MOF requires no parameter and embraces the density concept. Experimental results show that top-10 highest scores from MOF could identify all outliers from synthesized datasets similar to those scores from the state-of-the-art outlier scoring methods such as LOF and FastABOD. Moreover, it could retrieve more outliers from three real World datasets.