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|Title:||Efficient utility-based clustering over high dimensional partition spaces|
|Keywords:||Bayesian;Circardian Expression Profiles;Genetics;Posterior Probability Distribution|
|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.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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