DenMune: Density peak based clustering using mutual nearest neighbors

TL;DR

DenMune is a novel density-based clustering algorithm that effectively handles arbitrary shapes, varying densities, and unbalanced classes by utilizing mutual nearest neighborhoods, requiring only one parameter and automatically detecting noise.

Contribution

It introduces a stable, parameter-efficient clustering method based on mutual nearest neighborhoods that automatically detects noise and adapts to different data complexities.

Findings

Robust performance on diverse datasets

Automatically detects and removes noise

Requires only one parameter, K

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.