Functional Clustering and Identifying Substructures of Longitudinal Data
纵向数据的函数聚类与子结构识别
Summary A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-centres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a non-parametric random-effect model of the truncated Karhunen–Loève expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We…