论文标题
稀疏多元功能数据的强大两层分区聚类
Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data
论文作者
论文摘要
提出了稀疏多元功能数据的新型弹性时间距离，并用于开发一种基于距离的两层分区聚类方法。使用此建议的距离，新方法不仅可以检测到异常设置下稀疏多元功能数据的正确簇，而且还可以检测不属于任何群集的异常值。基于噪声（DBSCAN），集聚层次聚类和$ K $ Medoids的应用程序的基于密度的空间聚类，例如基于密度的空间聚类和$ K $ Medoids，基于经典距离的聚类方法将扩展到基于新验证的稀疏多元功能案例。对模拟数据的数值实验强调，所提出的算法的性能优于现有基于模型和扩展距离方法的性能。使用西北太平洋旋风将数据作为例子证明了所提出的方法的有效性。
A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this proposed distance, the new approach not only can detect correct clusters for sparse multivariate functional data under outlier settings but also can detect those outliers that do not belong to any clusters. Classical distance-based clustering methods such as density-based spatial clustering of applications with noise (DBSCAN), agglomerative hierarchical clustering, and $K$-medoids are extended to the sparse multivariate functional case based on the newly-proposed distance. Numerical experiments on simulated data highlight that the performance of the proposed algorithm is superior to the performances of existing model-based and extended distance-based methods. The effectiveness of the proposed approach is demonstrated using Northwest Pacific cyclone tracks data as an example.
