Git: Clustering Based on Graph of Intensity Topology

• URL: http://arxiv.org/abs/2110.01274v1
• Date: Mon, 4 Oct 2021 09:29:43 GMT
• Title: Git: Clustering Based on Graph of Intensity Topology
• Authors: Zhangyang Gao, Haitao Lin, Cheng Tan, Lirong Wu, Stan. Z Li
• Abstract summary: We propose a novel clustering algorithm, namely GIT (Clustering Based on textbfGraph of textbfIntensity textbfTopology) With fast local cluster detection, robust topo-graph construction and accurate edge-cutting, GIT shows attractive ARISE performance.
• Score: 25.679620842010422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

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