The validation of biclustering algorithms remains a challenging task, even though a number of measures have been proposed for evaluating the quality of these algorithms. Although no criterion is universally accepted as the overall best, a number of meta-evaluation conditions to be satisfied by biclustering algorithms have been enunciated. In this work, we present MOCICE-BCubed F1, a new externa...

Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan [15] where one seeks to cluster cases and variables simultaneously. Such structures are also prevalent in block modeling of networks. In this paper, we develop a theory for the estimation and completion of matrices with biclustering structures, where the data is a partially observed and noise cont...

In this paper, we present a new algorithm called, BiBinConvmean, for biclustering of binary microarray data. It is a novel alternative to extract biclusters from sparse binary datasets. Our algorithm is based on Iterative Row and Column Clustering Combination (IRCCC) and Divide and Conquer (DC) approaches, K-means initialization and the CroBin evaluation function [6]. Applied on binary syntheti...

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 algorithms perform simultaneous row and column clustering of a given data matrix. In gene expression dataset a bicluster is a subset of genes that exhibit similar expression patterns through a subset of conditions. Biclustering is a useful data mining technique for identifying local patterns from gene expression data. In this paper biclusters are identified in two steps. In the fir...

an important step in considering of gene expression data is obtained groups of genes that have similarity patterns. biclustering methods was recently introduced for discovering subsets of genes that have coherent values across a subset of conditions. the las algorithm relies on a heuristic randomized search to find biclusters. in this paper, we introduce biclustering las algorithm and then appl...

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...

Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing s...