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Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)

Gisele L. Pappa, Alex Freitas




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 | #9489672 in Books |  Springer |  2012-03-14 |  2012-03-14 | Original language:English | PDF # 1 |  9.25 x.46 x6.10l,.64 | File type: PDF | 187 pages

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||1 of 1 people found the following review helpful.| Solid book on data mining via generative rule induction|By tangent|This book covers data mining via automatic, evolutionary generation of rule induction algorithms using context based process grammar. It is relatively short at around 200 pages, the presence of equations and algorithms is relatively sparse but the ones which are included are useful and clearly presented.
<| ||From the reviews:|"The book is targeted at researchers and postgraduate students. As the amount of data being mined continues to grow it demands ever more sophisticated mining algorithms. Therefore there is a need for new algorithms and so Pappa and

This unique text seeks to automate the design of a data mining algorithm. It first overviews data mining and evolutionary algorithms then discusses the design of a new genetic programming system for automating the design of full rule induction algorithms.



You easily download any file type for your gadget.Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)   |  Gisele L. Pappa, Alex Freitas. I really enjoyed this book and have already told so many people about it!

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