Dwof: A Robust Density-Based Outlier Detection Approach

The problem of unsupervised outlier detection is challenging, especially when the structure of data is unknown. This paper presents a new density-based outlier detection technique that detects the top-n outliers. It overcomes the limitations of existing approaches, like low accuracy and high sensitivity to parameters. Our approach provides a score to each object called Dynamic-Window Outlier Factor (DWOF). DWOF improves Resolution-based Outlier Factor method (ROF) to consider varying-density clusters, which improves outliers' ranking even when providing same outliers. Experiments show that DWOF's average accuracy is better than existing approaches and less sensitive to its parameter.