A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Besides classical clustering methods such as hierarchical clustering, in recent years
biclustering has become a popular approach to analyze biological data sets, eg gene …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Background In a number of domains, like in DNA microarray data analysis, we need to
cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify …

DNA microarray technologies are used extensively to profile the expression levels of
thousands of genes under various conditions, yielding extremely large data-matrices. Thus …

Background Biclustering has been largely applied for the unsupervised analysis of
biological data, being recognised today as a key technique to discover putative modules in …

Motivation: In recent years, there have been various efforts to overcome the limitations of
standard clustering approaches for the analysis of gene expression data by grouping genes …

Biclustering extends the traditional clustering techniques by attempting to find (all)
subgroups of genes with similar expression patterns under to-be-identified subsets of …