Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees

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Survey of clustering algorithms (2005, background): “Generally, cluster analysis of gene expression data is composed of two aspects: clustering genes [80], [206], [260], [268], [283], [288] or clustering tissues or experiments [5], [109], [238].”
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, neutral): “The traditional clustering techniques attempt to, in the context of microarray data analyses, partition a set of genes into ‘clusters’ with similar expression patterns under specified conditions (3), or identify such clusters from an otherwise unstructured microarray data set ( 4 ).”
Advances in Intelligent Data Analysis V (2003, methodology): “Mantaras Distance can be used as a variation of the original Fayyad and Irani’s proposal [13] and Van de Merckt’s contrast measure provides another alternative [24].”