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Functional clustering by Bayesian wavelet methods
Author(s) -
Ray Shubhankar,
Mallick Bani
Publication year - 2006
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2006.00545.x
Subject(s) - dirichlet process , cluster analysis , conjugate prior , wavelet , pattern recognition (psychology) , artificial intelligence , computer science , prior probability , gibbs sampling , data mining , mathematics , machine learning , bayesian probability
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.