Cluster and Classification Techniques for the Biosciences [Paperback]

A 2006 guide to use of clustering and classification statistical methods for biologists, from ecologists to bioinformaticians.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.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.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.1. Introduction; 2. Exploratory data analysis; 3. Cluster analysis; 4. Introduction to classification; 5. ClassificalÃ