Normal view MARC view ISBD view

Applied biclustering methods for big and high-dimensional data using R

Contributor(s): Kasim, Adetayo [Editor] | Shkedy, Ziv [Editor] | Kaiser, Sebastian [Editor] | Hochreiter, Sepp [Editor] | Talloen, Willem [Editor].
Material type: materialTypeLabelBookPublisher: London CRC Press 2017Description: xxv, 401 p. With index.ISBN: 9781482208238.Subject(s): R (Computer program language) | Cluster set theory | Big dataDDC classification: 005.7 Summary: 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. https://www.crcpress.com/Applied-Biclustering-Methods-for-Big-and-High-Dimensional-Data-Using-R/Kasim-Shkedy-Kaiser-Hochreiter-Talloen/p/book/9781482208238
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
General Stacks
Slot 83 (0 Floor, West Wing) Non-fiction 005.7 A7 (Browse shelf) Available 198238

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.

https://www.crcpress.com/Applied-Biclustering-Methods-for-Big-and-High-Dimensional-Data-Using-R/Kasim-Shkedy-Kaiser-Hochreiter-Talloen/p/book/9781482208238

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha