Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10629
Title: Efficient utility-based clustering over high dimensional partition spaces
Authors: Liverani, S
Anderson, PE
Edwards, KD
Millar, AJ
Smith, JQ
Keywords: Bayesian;Circardian Expression Profiles;Genetics;Posterior Probability Distribution
Issue Date: 2009
Publisher: International Society for Bayesian Analysis (ISBA)
Citation: Bayesian Analysis, 2009, 4 (3), pp. 539 - 572
Abstract: Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible. However, when each cluster in a partition has a signature and it is known that some signatures are of scientific interest whilst others are not, it is possible, within a Bayesian framework, to develop search algorithms which are guided by these cluster signatures. Such algorithms can be expected to find better partitions more quickly. In this paper we develop a framework within which these ideas can be formalized. We then briefly illustrate the efficacy of the proposed guided search on a microarray time course data set where the clustering objective is to identify clusters of genes with different types of circadian expression profiles.
URI: http://bura.brunel.ac.uk/handle/2438/10629
DOI: http://dx.doi.org/10.1214/09-BA420
ISSN: 1936-0975
1931-6690
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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