Automating the Design of Data Mining Algorithms An Evolutionary Comp
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 de
- PDF / 2,327,850 Bytes
- 197 Pages / 439.37 x 666.142 pts Page_size
- 86 Downloads / 216 Views
Advisory Board: S. Amari G. Brassard K.A. De Jong C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz Z. Michalewicz M.C. Mozer E. Oja G. P˘aun J. Reif H. Rubin A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras D. Whitley E. Winfree J.M. Zurada
For further volumes: http://www.springer.com/series/4190
Gisele L. Pappa r Alex A. Freitas
Automating the Design of Data Mining Algorithms An Evolutionary Computation Approach
Dr. Gisele L. Pappa Dept. de Ciência da Computação Universidade Federal de Minas Gerais Belo Horizonte Brazil [email protected]
Dr. Alex A. Freitas School of Computing University of Kent Canterbury UK [email protected]
Series Editors G. Rozenberg (Managing Editor) [email protected] Th. Bäck, J.N. Kok, H.P. Spaink Leiden Center for Natural Computing Leiden University Niels Bohrweg 1 2333 CA Leiden, The Netherlands A.E. Eiben Vrije Universiteit Amsterdam The Netherlands
ISSN 1619-7127 ISBN 978-3-642-02540-2 DOI 10.1007/978-3-642-02541-9 Springer Heidelberg Dordrecht London New York
e-ISBN 978-3-642-02541-9
Library of Congress Control Number: 2009939832 ACM Computing Classification (1998): I.2.6 © Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: KünkelLopka GmbH, Heidelberg Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
This book is dedicated to all the people who believe that learning is not only one of the most necessary but also one of the noblest human activities.
Preface
Data mining is a very active research area with many successful real-world applications. 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 difficult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining algorithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorit
Data Loading...
