Data Mining The Textbook
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining
- PDF / 17,227,663 Bytes
- 746 Pages / 504.48 x 719.76 pts Page_size
- 7 Downloads / 239 Views
Charu C. Aggarwal
Data Mining The Textbook
Charu C. Aggarwal IBM T.J. Watson Research Center Yorktown Heights New York USA
A solution manual for this book is available on Springer.com. ISBN 978-3-319-14141-1 ISBN 978-3-319-14142-8 (eBook) DOI 10.1007/978-3-319-14142-8 Library of Congress Control Number: 2015930833 Springer Cham Heidelberg New York Dordrecht London c Springer International Publishing Switzerland 2015 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. 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To my wife Lata, and my daughter Sayani
v
Contents
1 An Introduction to Data Mining 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Data Mining Process . . . . . . . . . . . . . . . . . . . . . 1.2.1 The Data Preprocessing Phase . . . . . . . . . . . . . . 1.2.2 The Analytical Phase . . . . . . . . . . . . . . . . . . . 1.3 The Basic Data Types . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Nondependency-Oriented Data . . . . . . . . . . . . . . 1.3.1.1 Quantitative Multidimensional Data . . . . . 1.3.1.2 Categorical and Mixed Attribute Data . . . 1.3.1.3 Binary and Set Data . . . . . . . . . . . . . 1.3.1.4 Text Data . . . . . . . . . . . . . . . . . . . 1.3.2 Dependency-Oriented Data . . . . . . . . . . . . . . . . 1.3.2.1 Time-Series Data . . . . . . . . . . . . . . . 1.3.2.2 Discrete Sequences and Strings . . . . . . . . 1.3.2.3 Spatial Data . . . . . . . . . . . . . . . . . . 1.3.2.4 Network and Graph Data . . . . . . . . . . . 1.4 The Major Building Blocks: A Bird’s Eye View . . . . . . . . . 1.4.1 Association Pattern Mining . . . . . . . . . . . . . . . 1.4.2 Data Clustering . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Outlier Detection . . . . . . . . . . . . . . . . . . . . . 1.4.4 Data Classification . . . . . . . . . . . . . . . . . . . . 1.4.5 Impact of Complex Data Types on Problem Definitions 1.4.5.1 Pattern Mining with Complex Data Types . 1.4.5.2 Clust
Data Loading...
