Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

Author:   Shafiq Alam (University of Auckland, New Zealand) ,  Gillian Dobbie (University of Auckland, New Zealand) ,  Yun Sing Koh (Auckland University of Technology, New Zealand University of Auckland, New Zealand University of Auckland, New Zealand University of Auckland, New Zealand) ,  Saeed Ur Rehman (Unitec Institute of Technology, New Zealand)
Publisher:   Information Science Reference
ISBN:  

9781306981385


Pages:   397
Publication Date:   01 January 2014
Format:   Electronic book text
Availability:   Available To Order   Availability explained
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Biologically-Inspired Techniques for Knowledge Discovery and Data Mining


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Overview

Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.

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Author:   Shafiq Alam (University of Auckland, New Zealand) ,  Gillian Dobbie (University of Auckland, New Zealand) ,  Yun Sing Koh (Auckland University of Technology, New Zealand University of Auckland, New Zealand University of Auckland, New Zealand University of Auckland, New Zealand) ,  Saeed Ur Rehman (Unitec Institute of Technology, New Zealand)
Publisher:   Information Science Reference
Imprint:   Information Science Reference
ISBN:  

9781306981385


ISBN 10:   1306981387
Pages:   397
Publication Date:   01 January 2014
Audience:   General/trade ,  General
Format:   Electronic book text
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

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