DenMune: Density peak based clustering using mutual nearest neighbors

Mohamed Abbas, Adel El-Zoghobi, Amin Shoukry
9/23/2023
cs.LG

Official code: scikit-learn-contrib/denmune-clustering-algorithm (34 stars, BSD-3-Clause, last commit 2025-03-07).

Abstract

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm, DenMune is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high-dimensional datasets relative to several known state-of-the-art clustering algorithms.

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Code Implementations(1)

scikit-learn-contrib/denmune-clustering-algorithmOfficial
BSD-3-Clause

DenMune a clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K (the number of nearest neighbors). The results show the superiority of DenMune. Enjoy the simplicty but the power of DenMune.

3412Python, Jupyter NotebookDec 21, 20211 years agoBSD-3-Clause
clusteringdeep-learningmachine-learningpython

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Cite this paper

@article{abbas2023denmune,
  title  = {DenMune: Density peak based clustering using mutual nearest neighbors},
  author = {Mohamed Abbas and Adel El-Zoghobi and Amin Shoukry},
  year   = {2023},
  eprint = {2309.13420},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.13420v1}
}

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