Clustering using objective functions and stochastic search
A new approach to clustering multivariate data, based on a multilevel linear mixed model, is proposed. A key feature of the model is that observations from the same cluster are correlated, because they share cluster-specific random effects. The inclusion of cluster-specific random effects allows parsimonious departure from an assumed base model for cluster mean profiles. This departure is captured statistically via the posterior expectation, or best linear unbiased predictor. One of the parameters in the model is the true underlying partition of the data, and the posterior distribution of this parameter, which is known up to a normalizing constant, is used to cluster the data. The problem of finding partitions with high posterior probability is not amenable to deterministic methods such as the EM algorithm. Thus, we propose a stochastic search algorithm that is driven by a Markov chain that is a mixture of two Metropolis–Hastings algorithms—one that makes small scale changes to individual objects and another that performs large scale moves involving entire clusters. The methodology proposed is fundamentally different from the well-known finite mixture model approach to clustering, which does not explicitly include the partition as a parameter, and involves an independent and identically distributed structure.
Keywords: Bayesian model; Best linear unbiased predictor; Cluster analysis; Hastings algorithm; Linear mixed model; Markov chain Monte Carlo methods; Metropolis; Microarray; Quadratic penalized splines; Set partition; Yeast cell cycle
Document Type: Research Article
Affiliations: 1: Cornell University, Ithaca, USA 2: University of Florida, Gainesville, USA
Publication date: February 1, 2008