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Robust Density Peaks Clustering for Manifold Data With Multiple Peaks.

Ling Ding, Chao Li, Shifei Ding

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 31, 2025
    PubMed
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    This study introduces a robust density peaks clustering (RDPCM) algorithm to overcome limitations in manifold data clustering. RDPCM enhances accuracy by using geodesic distance and adaptive cluster selection, improving upon traditional methods.

    Area of Science:

    • Data Science
    • Machine Learning
    • Computational Geometry

    Background:

    • Density Peaks Clustering (DPC) is a powerful unsupervised learning algorithm.
    • Traditional DPC struggles with manifold data, Euclidean distance limitations, and parameter sensitivity.
    • Manual cluster center selection in DPC can lead to suboptimal results.

    Purpose of the Study:

    • To develop a Robust Density Peaks Clustering algorithm (RDPCM) for manifold data with multiple peaks.
    • To enhance clustering accuracy and reduce sensitivity to parameters.
    • To improve the performance of density-based clustering on complex datasets.

    Main Methods:

    • Replaced Euclidean distance with geodesic distance, optimized via improved mutual K-nearest neighbors.
    • Incorporated manifold structure considerations for more accurate local density estimation.

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  • Introduced Minimum Spanning Tree-based Davies-Bouldin Index (MDBI) for adaptive cluster number selection.

    • Main Results:

    • RDPCM demonstrates superior performance on manifold data compared to existing advanced clustering algorithms.
    • The algorithm effectively handles datasets with multiple peaks and complex structures.
    • Reduced sensitivity to parameters like cutoff distance (dc) was observed.

    Conclusions:

    • RDPCM offers a more robust and accurate clustering solution for manifold data.
    • The proposed geodesic distance and adaptive cluster selection methods significantly improve upon DPC.
    • RDPCM provides a valuable advancement in unsupervised learning for complex data distributions.