Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

The latest advances in high-throughput techniques during the past decade allowed the
systems biology field to expand significantly. Today, the focus of biologists has shifted from …

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

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 …

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool
for biclustering or identifying interpretable row–column associations within high-dimensional …

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 …

High‐dimensional data, ie, data described by a large number of attributes, pose specific
challenges to clustering. The so‐called 'curse of dimensionality', coined originally to …

Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …

High-order clustering aims to identify heterogeneous substructures in multiway datasets that
arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex …

In the biclustering problem, we seek to simultaneously group observations and features.
While biclustering has applications in a wide array of domains, ranging from text mining to …