Outlier detection, ranking, neighborhood sets, clustering
Rank based algorithms provide a promising approach for outlier detection, but currently used rank-based measures of outlier detection suffer from two deficiencies: first they take a large value from an object whose density is high even though the object may not be an outlier and second the distance between the object and its nearest cluster plays a mild role though its rank with respect to its neighbor. To correct for these deficiencies we introduce the concept of modified-rank and propose new algorithms for outlier detection based on this concept.
Huang, Huaming; Mehrotra, Kishan; and Mohan, Chilukuri K., "Outlier detection using modified-ranks and other variants" (2011). Electrical Engineering and Computer Science Technical Reports. 72.