论文标题
使用无监督的二进制树聚类多元功能数据
Clustering multivariate functional data using unsupervised binary trees
论文作者
论文摘要
我们为一类功能数据提供了基于模型的聚类算法，该算法可以是曲线或图像。随机功能数据实现可以在定义域中的离散（可能是随机点）以错误（可能是随机的）进行测量。这个想法是通过递归分裂观测来建造一组二进制树。组的数量是以数据驱动方式确定的。新算法为在线数据集提供了易于解释的结果和快速预测。模拟数据集中的结果揭示了各种复杂设置中的良好性能。该方法应用于在德国回旋处对车辆轨迹的分析。
We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations could be measured with error at discrete, and possibly random, points in the definition domain. The idea is to build a set of binary trees by recursive splitting of the observations. The number of groups are determined in a data-driven way. The new algorithm provides easily interpretable results and fast predictions for online data sets. Results on simulated datasets reveal good performance in various complex settings. The methodology is applied to the analysis of vehicle trajectories on a German roundabout.
