# DenMune: Density peak based clustering using mutual nearest neighbors

arXiv: https://arxiv.org/abs/2309.13420 · HTML: https://mlsanity.com/paper/2309.13420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Efficient Parameter-free Clustering Using First Neighbor Relations](https://mlsanity.com/paper/1902.11266/) (2019)
- [To Cluster, or Not to Cluster: An Analysis of Clusterability Methods](https://mlsanity.com/paper/1808.08317/) (2018)
- [Approximate k-nearest neighbour based spatial clustering using k-d tree](https://mlsanity.com/paper/1303.1951/) (2013)
- [A Novel and Efficient Data Point Neighborhood Construction Algorithm based on Apollonius Circle](https://mlsanity.com/paper/1809.10702/) (2018)

## Built on by (most cited first, 1 of 1)

- [PECANN: Parallel Efficient Clustering with Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2312.03940/) (2023)

## Abstract (arXiv)

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.