BlockDPC: Distributed density peak clustering for large-scale data

doi:10.1016/j.patcog.2026.113357

2026-02-24

PRE

DOI：10.1016/j.patcog.2026.113357

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摘要

En 中文

• 提出的BlockDPC平衡了时间复杂度和聚类精度。 • 我们为大规模数据聚类中的采样提供了坚实的理论依据。 • 与14种方法相比，BlockDPC在多数情况下实现了最佳性能。

Keyword:

BlockDPC

density peak clustering

large-scale data

distributed clustering

sampling theory

期刊

Pattern Recognition

IF:

7.6

论文数:

1.3W

被引数:

4.5W

机构

Qilu University of Technology

学者数:

1.1W

论文数: 8.9K

被引数: 16

Yangzhou University

学者数:

2.8W

论文数: 1.9W

被引数: 3.3W

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