Proven Methods for Big Data Analysis

As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix.

The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

chapter 2|24 pages

From Cluster Analysis to Biclustering

part |2 pages

I Biclustering Methods

chapter 3|12 pages

δ-Biclustering and FLOC Algorithm

chapter 4|12 pages

The xMotif algorithm

chapter 5|12 pages

Bimax Algorithm

chapter 7|10 pages

Spectral Biclustering

chapter 8|20 pages


chapter 9|12 pages

Iterative Signature Algorithm

part |2 pages

II Case Studies and Applications

chapter 13|10 pages

Integrative Analysis of miRNA and mRNA Data

part |2 pages

III R Tools for Biclustering

chapter 20|28 pages

The BiclustGUI Package

chapter 22|10 pages

Biclustering for Cloud Computing

chapter 23|12 pages

biclustGUI Shiny App