Motivation: During the last years, the discovering of biclusters in data is becoming more and
more popular. Biclustering aims at extracting a set of clusters, each of which might use a …

Motivations: Bi-clustering is an important approach in microarray data analysis. The
underlying bases for using bi-clustering in the analysis of gene expression data are (1) …

As today's organizations are capturing exponentially larger amounts of data than ever, now
is the time for organizations to rethink how they digest that data. Through advanced …

Recommender Systems present a high-level of sparsity in their ratings matrices. The
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …

In this review, we have discussed the class-prediction and discovery methods that are
applied to gene expression data, along with the implications of the findings. We attempted to …

Due to data sparseness and attribute redundancy in high-dimensional data, clusters of
objects often exist in subspaces rather than in the entire space. To effectively address this …

In order to establish consolidated standards in novel data mining areas, newly proposed
algorithms need to be evaluated thoroughly. Many publications compare a new proposition …

Background Biclustering, the discovery of sets of objects with a coherent pattern across a
subset of conditions, is a critical task to study a wide-set of biomedical problems, where …

The discovery of discriminative patterns from high-dimensional data offers the possibility to
learn from informative subspaces and pattern-centric features, paving the way to associative …

The increasing ability to collect data from urban environments, coupled with a push towards
openness by governments, has resulted in the availability of numerous spatio-temporal data …