Fuzzy clustering with spatial-temporal information
Fuzzy clustering with spatial-temporal information
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DOI：
10.1016/j.spasta.2019.03.002
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发表时间：
2019-04-01
影响因子：
2.3
通讯作者：
Massari, Riccardo
中科院分区：
文献类型：
--
作者：
D'Urso, Pierpaolo;De Giovanni, Livia;Massari, Riccardo
基于在多个时间场合观察到的一组定量特征对地理单元进行聚类需要处理空间和时间信息的复杂性。特别是，应该考虑（1）要聚类的单元的空间性质，（2）多元时间轨迹的空间特征，以及（3）基于上述复杂特征将地理单元分配给给定聚类的不确定性。本文讨论了一种新颖的空间约束多元时间序列聚类，用于以不同空间邻近程度为特征的单元。特别是，采用具有动态时间规整相异性度量和空间惩罚项的围绕中心点的模糊划分算法来对多元时空序列进行分类。使用模拟和实际数据对聚类方法进行了理论上的介绍和讨论，突出了其主要特征。特别是，在单元之间嵌入不同级别的邻近度的能力，以及考虑不同长度的时间序列的能力。 (c) 2019 Elsevier B.V. 保留所有权利。
Clustering geographical units based on a set of quantitative features observed at several time occasions requires to deal with the complexity of both space and time information. In particular, one should consider (1) the spatial nature of the units to be clustered, (2) the characteristics of the space of multivariate time trajectories, and (3) the uncertainty related to the assignment of a geographical unit to a given cluster on the basis of the above complex features. This paper discusses a novel spatially constrained multivariate time series clustering for units characterized by different levels of spatial proximity. In particular, the Fuzzy Partitioning Around Medoids algorithm with Dynamic Time Warping dissimilarity measure and spatial penalization terms is applied to classify multivariate Spatial-Temporal series. The clustering method has been theoretically presented and discussed using both simulated and real data, highlighting its main features. In particular, the capability of embedding different levels of proximity among units, and the ability of considering time series with different length. (c) 2019 Elsevier B.V. All rights reserved.