# Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing

arXiv: https://arxiv.org/abs/2401.11402 · HTML: https://mlsanity.com/paper/2401.11402/

## Key facts

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- First posted on arXiv 21 Jan 2024.

## Abstract (arXiv)

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.