The increasing availability of gene expression data has encouraged the development of purposely-built intelligent data analysis techniques. Grouping genes characterized by similar expression patterns is a widespread accepted – and often mandatory – analysis step. Despite the fact that a number of biclustering methods have been developed to discover clusters of genes exhibiting a similar express...

We present a framework for biclustering and clustering where the observations are general labels. Our approach is based on the maximum likelihood estimator and its convex relaxation, and generalizes recent works in graph clustering to the biclustering setting. In addition to standard biclustering setting where one seeks to discover clustering structure simultaneously in two domain sets, we show...

Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based on the current column memberships, and vice versa. We propose a biclustering algorithm that maximizes a global objective function using message passing. Our objective function c...

DNA microarray technology has generated a large number of gene expression data. Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. It finds clusters of genes possessing similar characteristics together with biological conditions creating these similarities. Almost all the current biclustering algorithms find bicluster in one microarray datase...

In this paper, the Biclustering analysis of coregulated biclusters from gene expression data is carried out. Gene expression is the process, which produces functional product from the gene information. Data mining is used to find relevant and useful information from databases. Clustering groups the genes according to the given conditions. Biclustering algorithms belong to a distinct class of cl...

We consider the problem of bipartite community detection in networks, or more generally the network biclustering problem. We present a fast two-stage procedure based on spectral initialization followed by the application of a pseudo-likelihood classifier twice. Under mild regularity conditions, we establish the weak consistency of the procedure (i.e., the convergence of the misclassification ra...

Most biclustering algorithms for microarrays data analysis focus on positive correlations of genes. However, recent studies demonstrate that groups of biologically significant genes can show negative correlations as well. So, discovering negatively correlated patterns from microarrays data represents a real need. In this paper, we propose a Memetic Biclustering Algorithm (MBA) which is able to ...

MOTIVATION Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably...

Biclustering of the gene expressing data is an important task in bioinformatics. By clustering the gene expressing data obtained under different experimental conditions, function and regulatory elements of the gene sequence can be analyzed and recognized. A parallel biclustering algorithm for gene expressing data is presented. Based on the anti-monotones property of the quality of the data sets...

The paper presents a novel approach for customer segmentation which is the basic issue for an effective CRM ( Customer Relationship Management ). Firstly, the chi-square statistical analysis is applied to choose set of attributes and K-means algorithm is employed to quantize the value of each attribute. Then DBSCAN algorithm based on density is introduced to classify the customers into three gr...