Proactive Data Mining with Decision Trees
This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to sugges
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Haim Dahan • Shahar Cohen • Lior Rokach Oded Maimon
Proactive Data Mining with Decision Trees
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Haim Dahan Dept. of Industrial Engineering Tel Aviv University Ramat Aviv Israel
Lior Rokach Information Systems Engineering Ben-Gurion University Beer-Sheva Israel
Shahar Cohen Dept. of Industrial Engineering & Management Shenkar College of Engineering and Design Ramat Gan Israel
Oded Maimon Dept. of Industrial Engineering Tel Aviv University Ramat Aviv Israel
ISSN 2191-8112 ISSN 2191-8120 (electronic) ISBN 978-1-4939-0538-6 ISBN 978-1-4939-0539-3 (eBook) DOI 10.1007/978-1-4939-0539-3 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2014931371 © The Author(s) 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To our families
Preface
Data mining has emerged as a new science—the exploration, algorithmically and systematically, of data in order to extract patterns that can be used as a means of supporting organizational decision making. Data mining has evolved from machine learning and pattern recognition theories and algorithms for modeling data and extracting patterns. The underlying assumption of the inductive approach is that the trained model is applicable to
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