Clustering is the process of grouping a set of objects into classes of similar objects. Although
definitions of similarity vary from one clustering model to another, in most of these models …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

While within-cluster information is commonly utilized in most soft subspace clustering
approaches in order to develop the algorithms, other important information such as between …

Biclustering consists in simultaneous partitioning of the set of samples and the set of their
attributes (features) into subsets (classes). Samples and features classified together are …

Clustering suffers from the curse of dimensionality, and similarity functions that use all input
features with equal relevance may not be effective. We introduce an algorithm that discovers …

Data mining is an inevitable task in most of the emerging computing technologies as it
debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the …

In this paper we introduce a novel algorithm called TRICLUSTER, for mining coherent
clusters in three-dimensional (3D) gene expression datasets. TRICLUSTER can mine …

Recommender systems have been widely used in various domains including movies, news,
music with an aim to provide the most relevant proposals to users from a variety of available …

The analysis of protein-protein interactions is fundamental to the understanding of cellular
organization, processes, and functions. Recent large-scale investigations of protein-protein …

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-
dimensional datasets, and has been successfully applied in many domains. In recent years …