Optimal search space for clustering gene expression data via consensus.
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.
Department of Intelligent Data Analysis, Brunel University, Uxbridge, United Kingdom. Michael.Hirsch@brunel.ac.uk
Michael
M
Hirsch
Stephen
S
Swift
Xiohui
X
Liu
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D015999:Multivariate Analysis; D010963:Plasmodium falciparum / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics