Applied Biclustering Methods for Big and High-Dimensional Data Using R Hardback
Edited by Adetayo (Wolfson Research Institute for Health and Wellbeing, Durham University, UK) Kasim, Ziv (Hasselt Univeristy, Diepenbeek, Belgium) Shkedy, Sebastian (Ludwig Maximilian Universitat, Munich, Germany) Kaiser, Sepp (Johannes Kepler University Linz, Austria) Hochreiter, Willem (Janssen Pharmaceuticals, Beerse, Belgium) Talloen
Part of the Chapman & Hall/CRC Biostatistics Series series
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.
- Format: Hardback
- Pages: 407 pages, 12 Tables, black and white; 117 Illustrations, black and white
- Publisher: Apple Academic Press Inc.
- Publication Date: 19/09/2016
- Category: Probability & statistics
- ISBN: 9781482208238