一个强大的交替最小平方K-平均数对时间序列的集群方法,使用动态时间扭曲不相似性
J Fernando Vera-Vera1, J Antonio Roldán-Nofuentes1
1Department of Statistics and O.R., University of Granada, Faculty of Sciences, Fuentenueva s/n, 18071, Granada, Spain.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
本研究介绍了一种强大的K-平均时间序列的集群方法,使用动态时间扭曲处理不平等的长度和缺失的数据. 与传统方法相比,新方法改善了大型数据集的维度减少.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 统计 统计 统计 统计
背景情况:
- 时间序列聚类在各个学科中至关重要.
- 像K-means这样的标准方法与不平等的长度或缺失的数据作斗争.
- 动态时间扭曲 (DTW) 为基于形状的分析提供了弹性不相似性.
研究的目的:
- 为时间序列的K-平均集群开发一个缩小维度的程序.
- 用复杂的时间序列数据解决传统集群方法的局限性.
- 为了提高对具有不平等或不完整时间序列的大型数据集的聚类性能.
主要方法:
- 提出了一种新的K-means集群程序,可靠地减少维度.
- 通过交替最小正方形利用平方DTW不相似性的辅助配置.
- 与经典的多维缩放和基于模型的集群进行性能比较.
主要成果:
- 拟议的K-means程序略有改善了经典的多维缩放.
- 这两种K-means方法都在降低维度方面表现出稳定性,适用于大型数据集.
- 基于模型的聚类在高维度中表现不佳,即使在缩小的维度中也是如此.
结论:
- 新的K-means方法提供了改进的,强大的时间序列聚类,特别是在大型,复杂的数据集.
- 该方法有效地处理了基于DTW的集群固有的维度减少挑战.
- 它在处理高维时间序列数据时,提供了基于模型的聚类的优质替代方案.
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