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Cluster and Classification Techniques for the Biosciences
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Cluster and Classification Techniques for the Biosciences Paperback - 2006

by Alan H. Fielding

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Recent 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 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques.

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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.

Details

  • Title Cluster and Classification Techniques for the Biosciences
  • Author Alan H. Fielding
  • Binding Paperback
  • Edition 1st
  • Pages 260
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2006-12-14
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Table of Contents
  • ISBN 9780521618007 / 0521618002
  • Weight 1.26 lbs (0.57 kg)
  • Dimensions 9.74 x 6.34 x 0.53 in (24.74 x 16.10 x 1.35 cm)
  • Category Science
  • Library of Congress subjects Biology, Data Interpretation, Statistical
  • Library of Congress Catalogue Number 2006027983
  • Dewey Decimal Code 570.285
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Cluster and Classification Techniques for the Biosciences
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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H.

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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H

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Cluster and Classification Techniques for the Biosciences
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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H

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Cluster and Classification Techniques for the Biosciences
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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H

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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H

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Cluster and Classification Techniques for the Biosciences

Cluster and Classification Techniques for the Biosciences

by Alan H. Fielding

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Paperback / softback. New. This 2006 book provides an overview of clustering and classification methods (from long-established statistical methods to more recent machine learning techniques), written specifically for bioscientists. Examples are taken from across the life sciences, including bioinformatics, and are used throughout to illustrate key concepts, and the potential of each technique discussed.
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Cluster and Classification Techniques for the Biosciences
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Cluster and Classification Techniques for the Biosciences

by Alan H. Fielding

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Cluster and Classification Techniques for the Biosciences

by Fielding, Alan H.

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Cluster and Classification Techniques for the Biosciences

by Alan H. Fielding

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paperback. ISBN: 9780521618007
Cambridge University Press, 15 January 2007
Paperback, 260 pages
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… Read more
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