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http://bura.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 Electrical Engineering Research Papers |
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