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Automating the Design of Data Mining Algorithms - An Evolutionary Computation Approach (Hardcover, 2010 ed.) Loot Price: R2,883
Discovery Miles 28 830
Automating the Design of Data Mining Algorithms - An Evolutionary Computation Approach (Hardcover, 2010 ed.): Gisele L. Pappa,...

Automating the Design of Data Mining Algorithms - An Evolutionary Computation Approach (Hardcover, 2010 ed.)

Gisele L. Pappa, Alex Freitas

Series: Natural Computing Series

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Loot Price R2,883 Discovery Miles 28 830 | Repayment Terms: R270 pm x 12*

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Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Natural Computing Series
Release date: November 2009
First published: 2010
Authors: Gisele L. Pappa • Alex Freitas
Dimensions: 235 x 155 x 17mm (L x W x T)
Format: Hardcover
Pages: 187
Edition: 2010 ed.
ISBN-13: 978-3-642-02540-2
Categories: Books > Computing & IT > General theory of computing > Data structures
Books > Computing & IT > Computer programming > Algorithms & procedures
Books > Computing & IT > Applications of computing > Databases > Data mining
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LSN: 3-642-02540-4
Barcode: 9783642025402

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