Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

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 …

Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

DNA microarray technology has now made it possible to simultaneously monitor the
expression levels of thousands of genes during important biological processes and across …

Motivation: In recent years, there have been various efforts to overcome the limitations of
standard clustering approaches for the analysis of gene expression data by grouping genes …

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 …

With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory
interactions, and apply the method to predict a large portion of the regulatory network of the …