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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3222

Title: Optimal search space for clustering gene expression data via consensus
Authors: Hirsch, M
Swift, S
Liu, X
Keywords: Ensemble clustering
fitness function
gene expression data
greedy search
search space ristriction
Publication Date: 2007
Publisher: Mary Ann Liebert
Citation: The Journal of Computational Biology 14 (10): 1327-1341, Dec 2007
Abstract: Ensemble clustering methods have become increasingly important to ease the task of choosing the most appropriate cluster algorithm for a particular data analysis problem. The consensus clustering (CC) algorithm is a recognized ensemble clustering method that uses an artificial intelligence technique to optimize a fitness function. We formally prove the existence of a subspace of the search space for CC, which contains all solutions of maximal fitness and suggests two greedy algorithms to search this subspace. We evaluate the algorithms on two gene expression data sets and one synthetic data set, and compare the result with the results of other ensemble clustering approaches.
URI: http://bura.brunel.ac.uk/handle/2438/3222
ISSN: 1066-5277
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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