Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference. Our...

Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of biclustering algorithms relatively fast can be used as reference for comparison several BCBimax algorithm. The algorithm works sub-matrix containing element 1 formed binary data matrix. selection thresholds in binarization process minimum combination columns...

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.05.001 ⇑ Corresponding author. E-mail addresses: [email protected] gmail.com (C.W. Yu), [email protected] (R.C.C. (H. Yan). In this paper, we present a hypergraph based geometric biclustering (HGBC) algorithm. In a high dimensional space, bicluster patterns to be recognized can be considered...

Molecular structure and Function program, Hospital for Sick Children, 555 University Avenue, Toronto ON, Canada M5G 1X8. Terrence Donnelly Centre for Cellular & Biomolecular Research (Donnelly CCBR), 160 College Street, Room 930 Toronto, Ontario Canada, ON M5S 3E1. Department of Molecular Genetics and Department of Biochemistry, University of Toronto, 1 Kings College Circle, Toronto ON, Canada ...

Biclustering is a powerful data mining technique that allows simultaneously clustering rows (observations) and columns (features) in matrix-format set, which can provide results checkerboard-like pattern for visualization exploratory analysis wide array of domains. Multiple biclustering algorithms have been developed the past two decades, among convex guarantee global optimum by formulating as ...

Biclustering algorithm on Gibbs sampling strategy is a recruit in the field of the analysis of gene expression data of microarray experiments. Its feasibility and validity still need to be researched not only for synthetic datasets but also for real datasets. Here we investigated a biclustering algorithm on a microarray dataset of Yeast genome through building a database for storing microarray ...

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of "coherence" patterns. These methods perform unsatisfactorily if the ...

In this work we introduce a comprehensive algorithmic pipeline for multiple parametric model estimation. The proposed approach analyzes the information produced by a random sampling algorithm (e.g., RANSAC) from a machine learning/optimization perspective, using a parameterless biclustering algorithm based on L1 nonnegative matrix factorization (L1-NMF). The proposed framework exploits consiste...

Biclustering is evolving into one of the major tools for analyzing large datasets given as matrix of samples times features. Biclustering has several noteworthy applications and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. FABIA is one of the most successful biclustering methods and is used by companies like Bayer, Janssen,...