Motivation Biclustering algorithms are commonly used for gene expression data analysis.
However, accurate identification of meaningful structures is very challenging and state-of-the …

Text data mining is the process of extracting valuable information from a dataset consisting
of text documents. Popular clustering algorithms do not allow detection of the same words …

Clustering consists of partitioning data objects into subsets called clusters according to
some similarity criteria. This paper addresses a structure for generating overlap** clusters …

Motivation In this paper, we present an open source package with the latest release of
Evolutionary-based BIClustering (EBIC), a next-generation biclustering algorithm for mining …

Biclustering is a branch of data analysis, whereby the goal is to find two–dimensional
subgroups in a matrix of scalars. We introduce a new approach for biclustering discrete and …

Biclustering is a technique that looks for patterns hidden in some columns and some rows of
the input data. Evolutionary search-based biclustering (EBIC) is probably the first …

Biclustering is a growing in popularity machine learning technique which searches for
patterns in subsets of rows and subsets of columns. One of the recent advances in …

Finding meaningful structures in big data is challenging, especially within big and noisy
data. In this short paper, we present the results of the application of 6 different biclustering …

Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering
algorithm for mining genetic data. The major contribution of this paper is adding support for …

La découverte et la gestion de motifs font référence à un ensemble d'activités de
prétraitement de données ainsi que d'extraction, de manipulation et de stockage de motifs à …