Biclustering is a simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (clusters). Samples and features clustered together are supposed to have a high relevance to each other. In this paper we provide a new mathematical programming formulation for unsupervised biclustering. The proposed model involves the solution of a fractional 0-1 programmin...

Biclustering has been suggested and found very useful to discover gene regulation patterns from gene expression microarrays. Several quantitative algorithms, among others CC and BIMAX, have been implemented in R, mainly by the biclust package. To our best knowledge, there have been so far no qualitative biclustering methods implemented. Therefore we introduce rqubic, a Bioconductor package impl...

Consistent biclusterings of sets of data are useful for solving feature selection and classification problems. The problem of finding a consistent biclustering can be formulated as a combinatorial optimization problem, and it can be solved by the employment of a recently proposed VNS-based heuristic. In this context, the concept of β-consistent biclustering has been introduced for dealing with ...

Conventional clustering technique for gene expression data provides a global view of the data. In the biological prospective, a local view is essential for better analysis of gene expression data with simultaneous grouping of genes and conditions. Several biclustering techniques have been proposed in the literature based on different problem formulation. Therefore, it is difficult to compare th...

Biclustering is an intrinsically challenging and highly complex problem, particularly studied in the biology field, where the goal is to simultaneously cluster genes and samples of an expression data matrix. In this paper we present a novel approach to gene expression biclustering by providing a binary Factor Graph formulation to such problem. In more detail, we reformulate biclustering as a se...

We apply spectral biclustering to mortality datasets in order to capture three relevant aspects: the period, the age and the cohort effects, as their knowledge is a key factor in understanding actuarial liabilities of private life insurance companies, pension funds as well as national pension systems. While standard techniques generally fail to capture the cohort effect, on the contrary, biclus...

Microarray technology produces large amounts of information to be manipulated by analysis methods, such as biclustering algorithms, to extract new knowledge. All-purpose multivariate data visualization tools are usually not enough for studying microarray experiments. Additionally, clustering tools do not provide means of simultaneous visualization of all the biclusters obtained. We present an i...