Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction
Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction
复制标题
将错位相关曲线聚类应用于 varved 湖沉积物以进行气候重建
DOI：
10.1007/s00477-016-1287-6
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发表时间：
2016
影响因子：
4.2
通讯作者：
V. Vitelli
中科院分区：
文献类型：
--
作者：
K. Abramowicz;Per Arnqvist;P. Secchi;S. S. Luna;S. Vantini;V. Vitelli
在本文中，我们介绍了一种新的功能聚类方法，装袋Voronoi K-中心点对齐（BVKMA）算法，同时聚类和对齐空间相关的曲线。它是一种非参数统计方法，不依赖于分布或依赖结构假设。该方法的动机是并应用于瑞典北方卡斯约恩湖的纹层（每年分层）沉积物数据，旨在推断过去的环境和气候变化。由此产生的集群和它们的时间动态显示出巨大的潜力，季节性气候解释，特别是冬季气候变化。
In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes.