在k-means集群中忽视特征缩放的影响
1Department of Statistics, Faculty of Science, Kasetsart University, Bangkok, Thailand.
PloS one
|December 6, 2024
概括
特征缩放对于k-means集群至关重要,尤其是在包含不同单位特征的数据集中. 适当的缩放提高了准确性和性能,与使用原始数据不同.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 集群算法的集群算法
背景情况:
- K-means 集群是一种流行的数据分区算法.
- 特性缩放通常是k-means的关键预处理步骤.
- 功能缩放对k-means性能的影响,不同功能单元的性能并不总是被充分理解.
研究的目的:
- 为了研究特征缩放对k-means集群性能的影响.
- 将各种特征缩放技术与使用原始数据进行比较.
- 为了确定具有不同和相同单位特征的数据集的最佳特征缩放策略.
主要方法:
- 使用原始数据和通过五种方法缩放的数据对数据集进行了K-means聚类:Z-score,Min-Max规范化,百分位变换,最大绝对缩放和RobustScaler.
- 使用准确性,精度,回忆和F-score等指标来评估性能.
- 实验对具有不同单位的特征的数据集和具有相同单位的特征的数据集进行了实验.
主要成果:
- 对于具有不同单位特征的数据集,特征缩放显著改善了k-means集群性能,而不是使用原始数据.
- 当特征具有相同的单元时,特征缩放产生了与使用原始数据类似的结果,只观察到微小的改进.
- 在具有不同单位的数据集的缩放方法中,Z-score标准化和百分位数转换显示出优越和可比的性能.
结论:
- 特性缩放是k-means集群的重要预处理步骤,特别是在用不同单位测量特征的数据集中.
- Z-score标准化和百分比转换是提高k-means集群精度和性能的有效缩放方法.
- 虽然特征缩放提供了好处,但当处理异质特征缩放时,其必要性和影响更为明显.
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