A novel approach to outlier detection and clustering on the ground of the distribution of distances between multidimensional points is presented. The basic idea is to eval uate the outlier factor for each data point.
A comparison with some popular outlier detection and clustering methods shows the superiority of our approach.
This work is licensed under a Creative Commons Attribution 4.0 International License.