通过局部密度进行快速稀疏的代表性树木分裂,用于大规模的集群.
Renmin Wang1, Jie Li2,3
1School of Information Engineering, Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China. wangrenmin@alu.cqu.edu.cn.
Scientific reports
|August 11, 2025
概括
这项研究引入了一个大规模集群的新框架,它可以自动找到集群,而不需要预先设置的数字. 它可以高效地处理庞大的数据集,提高复杂的数据挖掘任务的准确性和速度.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 计算统计学 计算统计学
背景情况:
- 大规模聚类至关重要,但具有挑战性,现有的方法往往无法平衡效率,准确性和适应性.
- 参数依赖性和计算复杂性阻碍了当前算法对大规模数据集的应用.
研究的目的:
- 开发一种新的,无参数的框架,以实现高效,准确的大规模聚类.
- 解决现有的集群算法在处理大,复杂和任意形状的数据分布方面的局限性.
主要方法:
- 一种无参数的集群发现方法,使用基于动态密度的分割.
- 一种混合采样分区策略,将随机采样与K-means集成为线性复杂性代表点提取.
- 以局部密度为驱动的最小跨度树 (MST) 细分用于基于密度峰值的自适应分区.
主要成果:
- 拟议的框架在大型合成和现实数据集 (高达2000万个样本) 上,与最新的方法相比,实现了更高的集群精度.
- 显著减少了运行时间,展示了卓越的效率.
- 展示了对不同采样比率的稳定性,并消除了对用户指定的参数的需求.
结论:
- 新的框架为大规模集群挑战提供了强大,高效和准确的解决方案.
- 它的无参数性质和适应性使其非常适合用于具有复杂数据结构的真实世界数据挖掘应用.
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