Description: Automating the Design of Data Mining Algorithms Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). An Evolutionary Computation Approach Author(s): Gisele L. Pappa, Alex Freitas Format: Hardback Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Germany Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K ISBN-13: 9783642025402, 978-3642025402 Synopsis 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.
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Book Title: Automating the Design of Data Mining Algorithms
Number of Pages: 187 Pages
Language: English
Publication Name: Automating the Design of Data Mining Algorithms: an Evolutionary Computation Approach
Publisher: Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
Publication Year: 2009
Subject: Computer Science
Item Height: 235 mm
Item Weight: 503 g
Type: Textbook
Author: Gisele L. Pappa, Alex Freitas
Series: Natural Computing Series
Item Width: 155 mm
Format: Hardcover
