Functional clustering and identifying substructures of longitudinal data
Functional clustering and identifying substructures of longitudinal data
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DOI：
10.1111/j.1467-9868.2007.00605.x
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发表时间：
2007-01-01
影响因子：
5.8
通讯作者：
Li, Pai-Ling
中科院分区：
文献类型：
--
作者：
Chiou, Jeng-Min;Li, Pai-Ling
提出了一种纵向数据的功能聚类（FC）方法，即k-centres FC。k-centres FC方法通过用重新分类步骤预测聚类隶属度来解释聚类之间差异的均值和模式。聚类隶属度预测基于截断karhunen - love展开的非参数随机效应模型，结合非参数迭代均值和协方差更新方案。研究表明，在导出的可识别性条件下，与传统聚类算法相比，所提出的k中心FC方法可以大大提高聚类质量。此外，通过探索每个聚类的均值和协方差函数，k-centres FC方法提供了对聚类结构的额外洞察，从而促进了功能聚类分析。通过模拟研究和数据应用，包括生长曲线和基因表达谱数据，证明了k-centres FC方法的实际性能。
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-Lobve expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We show that, under the identifiability conditions derived, the k-centres FC method proposed can greatly improve cluster quality as compared with conventional clustering algorithms. Moreover, by exploring the mean and covariance functions of each cluster, the k-centres FC method provides an additional insight into cluster structures which facilitates functional cluster analysis. Practical performance of the k-centres FC method is demonstrated through simulation studies and data applications including growth curve and gene expression profile data.