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 …

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 …

The need to analyze high-dimension biological data is driving the development of new data
mining methods. Biclustering algorithms have been successfully applied to gene expression …

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

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

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

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 …

Background Biclustering of gene expression data searches for local patterns of gene
expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression …

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

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 …