A recent proposal developed to avoid some of the drawbacks presented by standard clustering algorithms is the so-called biclustering technique [1], which performs clustering of rows and columns of the data matrix simultaneously, allowing the extraction of additional information from the dataset. Since the biclustering problem is combinatorial, and ant-based systems present several advantages wh...

Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated over the rest of the conditions. As tradition...

This article presents an algorithm for finding exact and overlapping biclusters, all covered by the same discrete value. It can be very useful to analyse data where the key task is finding which objects (elements in rows) across subset of attributes (elements in columns) have the same value in case of discrete data or belong to this same discrete state in case of continuous data.

Abstract Biclustering is a two-dimensional data analysis technique that, applied to matrix, searches for subset of rows and columns that intersect produce submatrix with given, expected features. Such an approach requires different methods those typical classification or regression tasks. In recent years it has become possible express biclustering goals in the form Boolean reasoning. This paper...

We present a probabilistic block-constant biclustering model that simultaneously clusters rows and columns of a data matrix. All entries with the same row cluster and column cluster form a bicluster. Each cluster is part of a mixture having a nonparametric Bayesian prior. The number of biclusters is therefore treated as a nuisance parameter and is implicitly integrated over during simulation. M...

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNAmicroarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of bicluste...

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the se...