一个新的密度峰集群算法用于基于K-最近邻居的集群中心的自动选择
Zhihe Wang1, Huan Wang1, Hui Du1
1The School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
一个新的密度峰值集群算法 (AKDPC) 自动选择集群中心,提高复杂数据集的准确性. 这种方法通过分类点和使用K-最近邻近距离来提高集群的密度估计.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 传统的密度峰集群 (DPC) 算法需要手动中心选择,限制了密度不同或形状非凸的复杂数据集的性能.
- 现有的DPC变体在自动集群中心识别方面扎,需要改进以实现更广泛的应用.
研究的目的:
- 提出一种新的密度峰集群算法 (AKDPC),用于自动选择集群中心.
- 在多样化和复杂的数据集上增强聚类准确性和稳定性.
主要方法:
- AKDPC根据相互的K-最近邻居 (KNN) 值将样品分为核心和非核心点.
- 密度取决于KNN的平均距离;较小的距离表示更高的密度.
- 集群中心从高密度的核心点中选择,并对非核心点进行代合并和分类.
主要成果:
- 在手动和现实数据集上的实验证明了AKDPC的有效性.
- 与经典集群算法和现有的DPC变体相比,AKDPC的准确性更高.
- 该算法成功处理具有不同密度和非凸结构的数据集.
结论:
- AKDPC提供了一种有效的解决方案,用于基于密度的聚类中的自动集群中心选择.
- 拟议的方法显著提高了集群精度,并克服了传统DPC算法的局限性.
- AKDPC为分析复杂数据结构提供了强大而准确的替代方案.
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