Biclustering is a very useful data mining technique for gene expression analysis and profiling. It helps identify patterns where different genes are co-related based on a subset of conditions. Bipartite Spectral partitioning is a powerful technique to achieve biclustering but its computation complexity is prohibitive for applications dealing with large input data. We provide a connection betwee...

The biclustering, co-clustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or sub-matrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering pr...

Given a gene expression data matrix where each cell is the expression level of a gene under a certain condition, biclustering is the problem of searching for a subset of genes that coregulate and coexpress only under a subset of conditions. The traditional clustering algorithms cannot be applied for biclustering as one cannot measure the similarity between genes (or rows) and conditions (or col...

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...

Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bio...

Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by using a biclustering algorithm. This method automatically identifies the groups of users that show similar browsing patterns under a specific subset of the pages. In ...

-The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Biclustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. In recent years, several biclustering methods have been suggested to identify local patterns in gene expression data. Most of these algorithms represent gr...

OBJECTIVE With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better...

A biclustering algorithm named Robust Biclustering Algorithm (ROBA) [2] has been used in a number of recent research works. The existing implementation of ROBA is not that time and space efficient. In this work, we develop a time and space efficient implementation of ROBA. We elicit some subtle properties of the base principle of ROBA to achieve these efficiencies. We reduce both time and space...