Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

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.

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.