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

Sunil Aryal, Jonathan R. Wells, Arbind Agrahari Baniya, KC Santosh
1/21/2024
cs.LG

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Abstract

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.

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Cite this paper

@article{aryal2024enabling,
  title  = {Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing},
  author = {Sunil Aryal and Jonathan R. Wells and Arbind Agrahari Baniya and KC Santosh},
  year   = {2024},
  eprint = {2401.11402},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.11402v1}
}

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