The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as 'simultaneous clustering' or 'co-clustering', has been successfully utilized to discover local patterns in gene expression data an...

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have ...

The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perpective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expre...

MOTIVATION Over the past decade, several biclustering approaches have been published in the field of gene expression data analysis. Despite of huge diversity regarding the mathematical concepts of the different biclustering methods, many of them can be related to the singular value decomposition (SVD). Recently, a sparse SVD approach (SSVD) has been proposed to reveal biclusters in gene express...

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 is used for discovering correlations among subsets of attributes with subsets of transactions in a transaction database. It has an extensive set of applications ranging from Gene co-regulation analysis[4], documentkeyword clustering[12] and collaborative filtering for online recommendation systems[13]. In this paper, we propose optimal biclustering problem as maximal crossing numbe...

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...

SUMMARY Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user i...

As websites increase in complexity, locating needed information becomes a difficult task. Such difficulty is often related to the websites’ design but also ineffective and inefficient navigation processes. Research in web mining addresses this problem by applying techniques from data mining and machine learning to web data and documents. In this study, the authors examine web usage mining, appl...