Abstract:
Outlier detection is a significant problem that has been studied in a variety of research and real-world applications. However, little research has been conducted on unsupervised parameter-free outlier scoring. This thesis proposes Mass ratio variance-based Outlier Factor, or MOF, which is unsupervised parameter-free outlier scoring for static data. This algorithm calculates outlier scores based on the variance of mass ratio. The data points with high outlier scores are associated with outliers while the data points with low outlier scores are associated with normal data points. This thesis also proposes Streaming Mass ratio variance-based Outlier Factor or SMOF. This algorithm calculates outlier scores based on MOF and the non-overlapping sliding window model which keeps the dense data points by weighted random sampling making highly efficient storage. Extensive experiments have been conducted to evaluate the performance of MOF and SMOF using synthesized and real-world data sets. The experimental results show that they have better accuracy than the state-of-the-art outlier detection techniques.
