Enabling clustering algorithms to detect clusters of varying densities
  through scale-invariant data preprocessing
Sunil Aryal Jonathan R. Wells Arbind Agrahari Baniya KC Santosh
In this paper, we show that preprocessing data using a variant of rank
transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)'
makes clustering algorithms robust to data representation and enable them to
detect varying density clusters. Our empirical results, obtained using three
most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density
Peak)-across a wide range of real-world datasets, show that clustering after
ARES transformation produces better and more consistent results.