Cluster and classification techniques for the biosciences / Alan H. Fielding.
By: Fielding, Alan [author.].
Contributor(s): Cambridge eBooks.
Publisher: Cambridge : Cambridge University Press, 2007Description: (xii, 246 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9780521618007 (paperback).Other title: Cluster & Classification Techniques for the Biosciences.Subject(s): Biology -- Data processing | Biology -- Classification | Cluster analysisGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | QH324.2 .F537 2007 (Browse shelf) | Available | AU0000000009267 |
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| QH324.2 .B549 2012 Bioinformatics in human health and heredity | QH324.2 .B56 2021 Bioinformatics : methods and applications / | QH324.2 .D38 2016 Biomedical Informatics : An Introduction to Information Systems and Software in Medicine and Health. / | QH324.2 .F537 2007 Cluster and classification techniques for the biosciences / | QH324.2 .T36 2018 Beginners guide to bioinformatics for high throughput sequencing / | QH324.9.C7 R33 2017 Life on ice : a history of new uses for cold blood / | QH325 .A724 2017 Life through time and space / |
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Exploratory data analysis -- Cluster analysis -- Introduction to classification -- Classification algorithms -- Other classification methods -- Classification accuracy.
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.