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A density-driven graph-based clustering model with adaptive outlier recognition
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DOI：10.1016/j.ipm.2025.104436.png)
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Abstract
En 中文
Clustering performance often deteriorates in the presence of outliers, especially when existing algorithms exhibit high parameter sensitivity and computational inefficiency. To address these limitations, we propose a density-driven graph-based clustering algorithm with adaptive outlier recognition. The method integrates local density estimation into a graph construction framework to dynamically identify and suppress outliers, ensuring that the learned affinity graph is based on reliable samples only. Furthermore, we introduce a non-negative matrix factorization (NMF)-based eigenvector approximation strategy, which reformulates the Laplacian rank constraint as a regularization term, thereby reducing both parameter dependence and computational burden. An iterative optimization scheme with theoretical convergence guarantees is developed to solve the resulting problem efficiently. Extensive experiments on two synthetic datasets and eleven real-world datasets—including Yale (165 samples), COIL100 (7200), ALOI (10800), CIFAR100 (50000) and MNIST (60000)—demonstrate that our method consistently ranks among the top three performers. Notably, it outperforms competitive baselines by approximately 5% on several datasets, and when 10% outliers are injected, it maintains leading performance with over 5% improvement over state-of-the-art methods. These results confirm the proposed algorithm’s robustness, scalability, and accuracy in both clean and noisy scenarios. The implementation of the proposed method is publicly available at: https://github.com/PhdJiayiTang/RCAL .
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