Identifying cluster number for subspace projected functional data clustering
Journal
Computational Statistics & Data Analysis
Journal Volume
55
Journal Issue
6
Start Page
2090-2103
ISSN
0167-9473
Date Issued
2011-06
Author(s)
Pai-Ling Li
Abstract
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. © 2011 Elsevier B.V. All rights reserved.
Subjects
Bootstrapping
Cluster analysis
Functional data analysis
Functional principal components
Gene expression profiles
Hypothesis test
Publisher
Elsevier BV
Type
journal article