一个有效的框架来获得最初的集群中心
B K Mishra1, Sachi Nandan Mohanty2, R R Baidyanath1
1Silicon Institute of Technology, Bhubaneswar, Odisha, 751024, India.
Scientific reports
|November 27, 2023
概括
改进数据挖掘集群,这项研究引入了新的方法,如最远跳跃中心选择 (FLCS),以找到比随机选择更好的初始集群中心,从而更准确地发现子组.
科学领域:
- 数据挖掘和机器学习
- 计算统计学 计算统计学
- 模式识别 模式识别
背景情况:
- 在数据挖掘中,集群对于无监督的模式发现至关重要.
- 集群中心的初始选择显著影响集群质量.
- 在K-Means中随机选择中心可以导致次优或错误的集群.
研究的目的:
- 提出选择初始集群中心的定性方法.
- 为了实现充分分离和事实上的集群.
- 改进现有的K-Means算法,以提高集群精度和效率.
主要方法:
- 开发和分析新的K-Means变体:远效K-means (FEKM),修改的中心K-means (MCKM),使用Quickhull (MFQ) 修改的FEKM和最远的跳跃中心选择 (FLCS).
- 使用集群有效性指标进行评估:丹恩指数,戴维斯-博尔丁指数,轮系数,兰德测量和V测量.
- 与经典K-Means相比,对收速度和计算复杂性的比较分析.
主要成果:
- FEKM和FLCS始终产生分离良好的集群中心.
- 与之前提出的方法相比,FLCS显示了更好的融合速度.
- 所有提出的方法,虽然比随机K-Means合速度稍慢,但产生了优异的集群结果.
结论:
- 对于初始集群中心选择的定性方法显著提高了集群性能.
- FLCS提供了精确的中心选择和更快的融合之间的平衡.
- 提出的方法对现实世界的数据集是有效的,在集群质量方面优于随机初始化.
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