Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Computational Statistics & Data Analysis - Special issue on classification
Robust estimation and classification for functional data via projection-based depth notions
Computational Statistics
Simultaneous curve registration and clustering for functional data
Computational Statistics & Data Analysis
A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
Computational Statistics & Data Analysis
k-mean alignment for curve clustering
Computational Statistics & Data Analysis
Principal components for multivariate functional data
Computational Statistics & Data Analysis
Functional data analysis in shape analysis
Computational Statistics & Data Analysis
Supervised classification for functional data: A weighted distance approach
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
We propose a new approach, the forward functional testing (FFT) procedure, to cluster number selection for functional data clustering. We present a framework of subspace projected functional data clustering based on the functional multiplicative random-effects model, and propose to perform functional hypothesis tests on equivalence of cluster structures to identify the number of clusters. The aim is to find the maximum number of distinctive clusters while retaining significant differences between cluster structures. The null hypotheses comprise equalities between the cluster mean functions and between the sets of cluster eigenfunctions of the covariance kernels. Bootstrap resampling methods are developed to construct reference distributions of the derived test statistics. We compare several other cluster number selection criteria, extended from methods of multivariate data, with the proposed FFT procedure. The performance of the proposed approaches is examined by simulation studies, with applications to clustering gene expression profiles.