Git: Clustering Based on Graph of Intensity Topology

TL;DR

GIT is a novel clustering algorithm that combines local intensity-based clustering with a global topological graph approach, effectively handling noise and scale variations while outperforming existing methods on multiple datasets.

Contribution

The paper introduces GIT, a new clustering method that integrates local intensity peaks and global topology, with automatic noise edge removal using Wasserstein distance, advancing clustering robustness and accuracy.

Findings

GIT outperforms seven competing algorithms on synthetic and real datasets.

GIT achieves about 10% higher F1-score on MNIST and FashionMNIST.

GIT demonstrates robustness to noise and scale variations.

Abstract

\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm. However, achieving these goals simultaneously is challenging, and most advanced approaches only focus on parts of them. Towards an overall consideration of these aspects, we propose a novel clustering algorithm, namely GIT (Clustering Based on \textbf{G}raph of \textbf{I}ntensity \textbf{T}opology). GIT considers both local and global data structures: firstly forming local clusters based on intensity peaks of samples, and then estimating the global topological graph (topo-graph) between these local clusters. We use the Wasserstein Distance between the predicted and prior class proportions to automatically cut noisy edges in the topo-graph and merge connected local clusters as final clusters. Then, we compare GIT with seven competing algorithms on five synthetic datasets and nine real-world datasets. With fast local cluster detection, robust topo-graph construction and accurate edge-cutting, GIT shows attractive ARISE performance and significantly exceeds other non-convex clustering methods. For example, GIT outperforms its counterparts about $10\%$ (F1-score) on MNIST and FashionMNIST. Code is available at \color{red}{https://github.com/gaozhangyang/GIT}.