# Git: Clustering Based on Graph of Intensity Topology

arXiv: https://arxiv.org/abs/2110.01274 · HTML: https://mlsanity.com/paper/2110.01274/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Online Deep Clustering for Unsupervised Representation Learning](https://mlsanity.com/paper/2006.10645/) (2020)
- [Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering](https://mlsanity.com/paper/1802.10549/) (2018)
- [Quickshift++: Provably Good Initializations for Sample-Based Mean Shift](https://mlsanity.com/paper/1805.07909/) (2018)
- [The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering](https://mlsanity.com/paper/1907.00680/) (2019)
- [RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density](https://mlsanity.com/paper/1611.00484/) (2016)

## Built on by (most cited first, 3 of 3)

- [PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding](https://mlsanity.com/paper/2302.07120/) (2023)
- [UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters](https://mlsanity.com/paper/2312.11323/) (2023)
- [Hierarchical clustering with maximum density paths and mixture models](https://mlsanity.com/paper/2503.15582/) (2025)

## Abstract (arXiv)

Accuracy, Robustness to noises and scales, Interpretability, Speed, and Easy 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 Graph of Intensity Topology). 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 colorredhttps://github.com/gaozhangyang/GIT.