Algorithms for Detecting Outliers via Clustering and Ranks

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 assign a large value to an object near a cluster whose density is high even through 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. Our method performs better than several density-based methods, on some synthetic data sets as well as on some real data sets.