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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