基于相对密度和集群间连接度的RISM集群算法
Ming Gong1, Yuqing Zhou2, Yan Ma3
1School of Education, Shanghai Normal University, Shanghai, China.
Scientific reports
|November 25, 2025
概括
我们介绍了RISM,这是一种用于复杂数据的新集群算法. 它有效地使用密度和连接性识别非线性数据集中的最佳集群数量.
科学领域:
- 无监督的机器学习
- 数据挖掘 数据挖掘
- 计算统计学 计算统计学
背景情况:
- 聚类复杂的非线性数据具有挑战性,特别是确定最佳的群集数量.
- 现有的方法与高维度和复杂的数据结构作斗争.
研究的目的:
- 介绍RISM (基于相对密度和集群间连接度的分割和合并),一种新的集群算法.
- 自动推断复杂数据集的最佳集群配置.
- 提高聚类准确性,对噪声的稳定性和可扩展性.
主要方法:
- RISM采用了两阶段的方法:分裂和合并.
- 分割阶段:使用一种新的相对密度度和相对距离来识别子集群.
- 合并阶段:包括原则性集群合并的集群间距离和连接度.
主要成果:
- 与九个最先进的算法相比,RISM表现出更高的性能.
- 在合成和现实世界数据集上实现了高集群精度.
- 显示了对噪声的强度和出色的可扩展性.
结论:
- RISM有效地解决了在复杂的非线性数据中确定最佳集群数量的挑战.
- 该算法的基于混合密度和连接性的方法提供了显著的优势.
- 在数据分析无监督学习方面,RISM是一个有前途的进步.
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