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
DOI: 10.4018/978-1-4666-6078-6
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793
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Description & Coverage
Description:

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.

Coverage:

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 & Statements

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.)
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Editor/Author Biographies
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.

Gillian Dobbie worked in industry for a couple of years before lecturing and doing research at the University of Melbourne, Victoria University of Wellington and the National University of Singapore. Her main areas of interest pertain to databases and the Web. She has worked in the foundations of database systems, defining logical models for various kinds of database systems, and reasoning about the correctness of algorithms in that setting. With colleagues at the National University of Singapore, she has defined a data model for semistructured data (called ORA-SS), providing a language independent description of the data. The group she was working with has used the ORA-SS data model to define a normal form for ORA-SS schema, defined valid views for semistructured databases, and described a storage structure for semistructured databases using object relational databases. Gill has a wide range of research interests, including databases, the Web, and software engineering. She is interested both in structured and semistructured data. More specifically, she is interested in how data can best be organized and managed, how the semantics of the data can be retained and expressed, and how querying can be carried out efficiently.

Yun Sing Koh is currently a senior lecturer at the Department of Computer Science, University of Auckland. After completing a Bachelor's degree in Computer Science and Masters in Software Engineering at University Malaya, she went on to do her PhD in Computer Science at University of Otago, New Zealand. Her current research interests include data mining, machine learning, and information retrieval. Most of her current research revolves around finding rare patterns/rules within datasets and data stream mining. She has also developed a keen interest in several other areas including particle swarm optimization, social network mining, and online auction fraud detection.

Saeed ur Rehman has submitted PhD thesis for examination at the University of Auckland, New Zealand. He has received his ME in Electrical and Electronic Engineering with first class honors from the University of Auckland, New Zealand and the B.Sc Electrical Engineering from NWFP University of Engineering and Technology, Pakistan in 2009 and 2004, respectively. He is currently working as a lecturer in Unitec Institute of Technology, Auckland, New Zealand. His doctoral research is focused on physical layer security of wireless networks. His ME thesis was on the Analytical and simulation analysis of MAC for Wireless Sensor Network. Following graduation, he has worked in cellular companies for three years as a Telecom Engineer (2005-08) and as a research assistant in CISTER/IPP-HURRAY Research unit, Portugal (2009). His current interests include cyber security, physical layer security of wireless networks and, in particular, analysis and design of radio fingerprinting for low cost transceivers.

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