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.On Secure Spectrum Sensing in Cognitive Radio Networks Using Emitters Electromagnetic Signature. ... Technical report, pages 09-02. Retrieved on 10 January, 2014 from http://www.cs.rpi.edu/research/pdf/09-02. pdf Ellis, K., aamp; Serinken, N. (2001). ... The impact of rf front-end characteristics on the spectral regrowth of communications signals. IEEE Transactions on Microwave Theory and Techniques, 53(6), 2179a2186. doi:10.1109/ TMTT.2005.848801 Hall, J., Barbeau, M., anbsp;...
|Title||:||Biologically-Inspired Techniques for Knowledge Discovery and Data Mining|
|Publisher||:||IGI Global - 2014-05-31|