Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …

Biclustering is an unsupervised classification technique that plays an increasingly important
role in the study of modern biology. This data mining technique has provided answers to …

In bioinformatics, biclustering is a crucial optimization task that can reveal hidden patterns
and identify groups of genes that behave similarly under certain conditions. This study aims …

Social media data analysis is a popular research domain since the last decade. Detecting
the events and sub-events from social media posts that require special attention is one of the …

The extraction of knowledge from large biological data is among the main challenges of
bioinformatics. Several data mining techniques have been proposed to extract data; in this …

Social media has become a nondetachable part of our life, with the exponential growth of
usage in the past decade. Social sites like Twitter, Facebook, Instagram, Flickr, Weibo, etc …

There are several goals of the two-dimensional data analysis: one may be interested in
searching for groups of similar objects (clustering), another one may be focused on …

Biclustering is a problem in machine learning and data mining that seeks to group together
rows and columns of a dataset according to certain criteria. In this work, we highlight the …

There is a growing interest in the offering of novel alternative choices to users of
recommender systems. These recommendations should match the target query while at the …