Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

Shafiq Alam (University of Auckland, New Zealand), Gillian Dobbie (University of Auckland, New Zealand), Yun Sing Koh (University of Auckland, New Zealand) and Saeed ur Rehman (Unitec Institute of Technology, New Zealand)
Indexed In: SCOPUS View 1 More Indices
Release Date: May, 2014|Copyright: © 2014 |Pages: 375
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793|DOI: 10.4018/978-1-4666-6078-6


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.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Ant colony optimization
  • Artificial Immune System
  • Artificial Neural Networks
  • Bee Colony Optimization
  • Fuzzy systems
  • Genetic Algorithms
  • Particle Swarm Optimization

Reviews and Testimonials

The editors have collected 15 papers on data mining techniques inspired by natural selection, animal behavior, and animals’ central nervous systems. The contributors examine probabilistic control of robots navigating random environments as the foraging activity of ants, particle swarm optimization for data clustering, ensemble learning models of artificial neural networks, a bee colony optimizer for predicting heating oil prices, automatic construction of relevance measures, and online monitoring of patient blood glucose levels.

– ProtoView Book Abstracts (formerly Book News, Inc.)

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Shafiq Alam received his Ph.D. degree from the University of Auckland and is currently working as a research fellow in the Department of Computer Science, University of Auckland. His research interests include optimization based data mining, web usage mining, and computational intelligence. He has two masters, one in Information Technology, and another in Computer Science. He has a B.Sc. in Computer Science. Shafiq Alam has held the positions of Lecturer, Assistant Professor, and Academic Coordinator at university level. He has been on the Program Committees of A-ranked data mining conferences and computational intelligence conferences.