Functional clustering by Bayesian wavelet methods

Authors: Ray, Shubhankar; Mallick, Bani

Source: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 68, Number 2, April 2006 , pp. 305-332(28)

Publisher: Wiley-Blackwell

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Abstract:

Summary.

We propose a nonparametric Bayes wavelet model for clustering of functional data. The wavelet-based methodology is aimed at the resolution of generic global and local features during clustering and is suitable for clustering high dimensional data. Based on the Dirichlet process, the nonparametric Bayes model extends the scope of traditional Bayes wavelet methods to functional clustering and allows the elicitation of prior belief about the regularity of the functions and the number of clusters by suitably mixing the Dirichlet processes. Posterior inference is carried out by Gibbs sampling with conjugate priors, which makes the computation straightforward. We use simulated as well as real data sets to illustrate the suitability of the approach over other alternatives.

Keywords: Functional clustering; Mixture of Dirichlet processes; Nonparametric Bayesian model; Wavelets

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2006.00545.x

Affiliations: 1: Texas A&M University, College Station, USA

Publication date: 2006-04-01