Applied Biclustering Methods for Big and High-Dimensional Data Using R |
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| Editor:
| Kasim, Adetayo
Shkedy, Ziv
Kaiser, Sebastian
Hochreiter, Sepp
Talloen, Willem |
| Series title: | Chapman and Hall/CRC Biostatistics Ser. |
| ISBN: | 978-1-315-35639-6 |
| Publication Date: | Oct 2016 |
| Publisher: | CRC Press LLC
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| Imprint: | Chapman & Hall/CRC |
| Book Format: | Digital (delivered electronically) |
| List Price: | USD $54.95 |
Book Description:
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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...
More Description
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.