Outlier detection using neighborhood radius based on fractal dimension

Partition outlier using neighborhood radius has proven to be an effective distance-based detection algorithm. However, it is not yet clear how to choose the neighborhood radius dmin, and getting the value by trial and error is still been widely adopted. This paper presents a method to get the neighborhood radius from fractal dimensions which is used to describe the self-similarity of a dataset. We first discuss how to calculate the fractal dimensions and how to value dmin, and then we use this value in distance-based outlier detection algorithms. Finally, we verify the validity of this neighborhood radius calculation method by experimental results.