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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 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.
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.
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