一种自动选择聚类中心的快速密度峰值聚类方法

Zhihe Wang, Yongbiao Li, Hui Du, Xiaofen Wei
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引用次数: 1

摘要

针对密度峰值聚类需要人工选择聚类中心的问题,提出了一种自动选择聚类中心的快速聚类方法。首先,我们的方法将数据分组,并根据其密度将每组标记为核心组或边界组。其次,通过迭代合并距离小于阈值的两个核心组来确定聚类,并在每个聚类中密度最大的位置选择聚类中心;最后,将边界组分配给最近的聚类中心对应的聚类。该方法消除了人工选择聚类中心的需要,与实验结果一致,提高了聚类效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fast Density Peak Clustering Method with Autoselect Cluster Centers
Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.
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