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Title details for Cluster and Classification Techniques for the Biosciences by Alan H. Fielding - Available

Cluster and Classification Techniques for the Biosciences

by Alan H. Fielding
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Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
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Publisher: Cambridge University Press

Kindle Book

  • Release date: December 14, 2006

OverDrive Read

  • ISBN: 9780511258077
  • Release date: December 14, 2006

PDF ebook

  • ISBN: 9780511258077
  • File size: 3801 KB
  • Release date: December 14, 2006

Formats

Kindle Book
OverDrive Read
PDF ebook
Kindle restrictions

subjects

Medical Nonfiction

Languages

English

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
Expand title description text
Medical Nonfiction