Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

This paper presents a new k-means type algorithm for clustering high-dimensional objects in
sub-spaces. In high-dimensional data, clusters of objects often exist in subspaces rather …

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 …

In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

This paper proposes a new method to weight subspaces in feature groups and individual
features for clustering high-dimensional data. In this method, the features of high …

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 …

Subspace clustering (SC) is a promising technology involving clusters that are identified
based on their association with subspaces in high-dimensional spaces. SC can be classified …