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Lookup NU author(s): Jianqiao Long, Dr Jichun Li
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The AuthorsFace clustering remains a challenging task due to the high intra-class variability and uneven density distributions inherent in real-world face datasets. These characteristics often undermine the performance of conventional clustering algorithms. To address these limitations, this paper introduces a novel density-based clustering method, termed DPC-MK (Density Peak Clustering with Mixed k-Nearest Neighbor strategy). Initially, the reverse nearest neighbors and shared nearest neighbors of each sample are identified based on the k-nearest neighbor (KNN) method, and their counts are quantitatively assessed. Subsequently, the distances between each sample and its k-nearest neighbors are computed to evaluate their respective contributions to the local density. The quantified reverse and shared neighbor counts are then integrated with the distance-based density metric to yield an enhanced local density estimate. Using this refined density, the relative distance between each sample and any other point with higher density is computed. A decision graph is then constructed from the modified local density and relative distance values to identify cluster centers. Finally, non-center points are assigned to clusters by following density gradients toward their nearest higher-density neighbors. The results of the ablation study clearly demonstrate the complementary roles of each component as well as the effectiveness of the method we proposed. The efficacy of DPC-MK is further validated on multiple UCI benchmark datasets and public face clustering datasets. Comparative evaluations against baseline and state-of-the-art algorithms—including K-means, DBSCAN, FCM, DPC, DPC-KNN, DPC-NN, DPC-FWSN, and LPMNN-DPC—demonstrate that DPC-MK achieves superior clustering performance and maintains robustness across diverse clustering scenarios and varying cluster counts, highlighting its strong generalization capability.
Author(s): Zhou Y, Cheng J, Long J, Li J, Li J, Li J
Publication type: Article
Publication status: Published
Journal: Neurocomputing
Year: 2025
Volume: 657
Print publication date: 07/12/2025
Online publication date: 20/09/2025
Acceptance date: 13/09/2025
Date deposited: 07/10/2025
ISSN (print): 0925-2312
ISSN (electronic): 1872-8286
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.neucom.2025.131576
DOI: 10.1016/j.neucom.2025.131576
Data Access Statement: Data will be made available on request.
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