Biclustering is a special case of subspace clustering that has become viable in several
domains. Particularly, in genomic data analysis, biclustering has been used to identify …

Biclustering is a powerful tool for exploratory data analysis in domains such as social
networking, data reduction, and differential gene expression studies. Topological learning …

Clustering has been used extensively in the analysis of high-throughput messenger RNA
(mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental …

Motivation Unsupervised learning approaches are frequently used to stratify patients into
clinically relevant subgroups and to identify biomarkers such as disease-associated genes …

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 …

Being an unsupervised machine learning and data mining technique, biclustering and its
multimodal extensions are becoming popular tools for analysing object-attribute data in …

Biclustering of biologically meaningful binary information is essential in many applications
related to drug discovery, like protein–protein interactions and gene expressions. However …

Bi-clustering is a widely used data mining technique for analyzing gene expression data. It
simultaneously groups genes and samples of an input gene expression data matrix to …

Many soft biclustering algorithms have been developed and applied to various biological
and biomedical data analyses. However, until now, few mutually exclusive (hard) …

To understand the underlying biological mechanisms of gene expression data, it is important
to discover the groups of genes that have similar expression patterns under certain subsets …