Cluster and Classification Techniques for the Biosciences

Alan H. Fielding

ISBN 10: 0521618002 / ISBN 13: 9780521618007

Published by Cambridge University Press, 2007

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9780521618007

Bibliographic Details

• Title: Cluster and Classification Techniques for the Biosciences
• Publisher: Cambridge University Press
• Publication Date: 2007
• Language: English
• ISBN 10: 0521618002
• ISBN 13: 9780521618007
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 244W x 170H x 14D millimeters 244 millimeters width by 170 millimeters height by 14 millimeters depth
• Weight: 455 grams

About this title

Synopsis

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.

About the Author

Alan H. Fielding is Senior Lecturer in the Division of Biology at Manchester Metropolitan University.