一个改进的弹性网集群算法与动态参数策略
Junyan Yi1, Maoming Wang2, Changsheng Zhou2
1Beijing University of Civil Engineering and Architecture, Beijing, 100044, China. yijunyan@bucea.edu.cn.
Scientific reports
|August 21, 2025
概括
这项研究引入了一种改进的弹性网络聚类算法 (IENDP),用于有效的数据挖掘. 这种新的方法提高了各种数据集的聚类质量,特别是大规模和高维的数据集,并降低了复杂性.
科学领域:
- 数据科学
- 机器学习
- 人工智能
背景情况:
- 聚类对于数据挖掘至关重要,但在实现多样化和大规模数据集的高质量解决方案方面仍然存在挑战.
- 高度的计算复杂性往往阻碍了有效的数据挖掘和知识发现.
研究的目的:
- 提出一个改进的弹性网络集群算法 (IENDP),解决当前集群方法的局限性.
- 提高从复杂数据集中发现结构和知识的能力.
主要方法:
- 开发了一种新能源函数,以更好地区分集群中的数据点分布.
- 整合了一个动态参数策略,以提高搜索能力和融合速度.
- IENDP算法是自我组织和自我学习的,不需要手动指导.
主要成果:
- IENDP算法实现了更高的集群质量,有效地识别了不同大小,形状和密度的集群.
- 与经典和最先进的集群算法相比,其表现优越.
- 在合成和现实数据集上显示了较低的计算和时间复杂性.
结论:
- 拟议的IENDP算法为高质量的聚类提供了有效的解决方案,特别是对于高维和大规模数据.
- 动态参数策略显著改善了集群性能,并降低了参数灵敏度.
- IENDP为数据挖掘和知识发现提供了强大而高效的方法.
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