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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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.
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