Graph-based data clustering via multiscale community detection

TL;DR

This paper introduces a graph-theoretical clustering method using multiscale community detection, which automatically estimates the number of clusters and improves performance over traditional methods.

Contribution

It combines graph construction with Markov Stability to enable multiscale clustering and parameter robustness, advancing data clustering techniques.

Findings

Multiscale graph clustering outperforms traditional methods.

The approach estimates the number of clusters automatically.

It reduces sensitivity to graph construction parameters.

Abstract

We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.