Genetic Algorithm Support to Data Mining
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David L. Olson · Dursun Delen
Advanced Data Mining Techniques
Dr. David L. Olson Department of Management Science University of Nebraska Lincoln, NE 68588-0491 USA [email protected]
ISBN: 978-3-540-76916-3
Dr. Dursun Delen Department of Management Science and Information Systems 700 North Greenwood Avenue Tulsa, Oklahoma 74106 USA [email protected]
e-ISBN: 978-3-540-76917-0
Library of Congress Control Number: 2007940052 c 2008 Springer-Verlag Berlin Heidelberg 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: WMX Design, Heidelberg Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com
I dedicate this book to my grandchildren. David L. Olson
I dedicate this book to my children, Altug and Serra. Dursun Delen
Preface
The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods.
Book Concept Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Part I: Introduction Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview
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