一种用于从稀疏和不完整轨迹数据中提取有限时间相干集的简易基于聚类的方法。
A rough-and-ready cluster-based approach for extracting finite-time coherent sets from sparse and incomplete trajectory data.
作者信息
Froyland Gary, Padberg-Gehle Kathrin
机构信息
School of Mathematics and Statistics, The University of New South Wales, Sydney, New South Wales 2052, Australia.
Technische Universität Dresden, Fachrichtung Mathematik, Institut für Wissenschaftliches Rechnen, D-01062 Dresden, Germany.
出版信息
Chaos. 2015 Aug;25(8):087406. doi: 10.1063/1.4926372.
We present a numerical method to identify regions of phase space that are approximately retained in a mobile compact neighbourhood over a finite time duration. Our approach is based on spatio-temporal clustering of trajectory data. The main advantages of the approach are the ability to produce useful results (i) when there are relatively few trajectories and (ii) when there are gaps in observation of the trajectories as can occur with real data. The method is easy to implement, works in any dimension, and is fast to run.
我们提出了一种数值方法，用于识别在有限时间内近似保留在移动紧凑邻域中的相空间区域。我们的方法基于轨迹数据的时空聚类。该方法的主要优点是，在（i）轨迹相对较少以及（ii）轨迹观测存在间隙（实际数据可能出现这种情况）时，仍能够产生有用的结果。该方法易于实现，适用于任何维度，并且运行速度快。
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