Data Mining

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Library of Congress Cataloging-in-Publication Data:

Kantardzic, Mehmed.

Data mining : concepts, models, methods, and algorithms / Mehmed Kantardzic. – 2nd ed.

p. cm.

ISBN 978-0-470-89045-5 (cloth)

1. Data mining. I. Title.

QA76.9.D343K36 2011

006.3'12–dc22

2011002190

oBook ISBN: 978-1-118-02914-5

ePDF ISBN: 978-1-118-02912-1

ePub ISBN: 978-1-118-02913-8

To Belma and Nermin

PREFACE TO THE SECOND EDITION

In the seven years that have passed since the publication of the first edition of this book, the field of data mining has made a good progress both in developing new methodologies and in extending the spectrum of new applications. These changes in data mining motivated me to update my data-mining book with a second edition. Although the core of material in this edition remains the same, the new version of the book attempts to summarize recent developments in our fast-changing field, presenting the state-of-the-art in data mining, both in academic research and in deployment in commercial applications. The most notable changes from the first edition are the addition of

new topics such as ensemble learning, graph mining, temporal, spatial, distributed, and privacy preserving data mining;
new algorithms such as Classification and Regression Trees (CART), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Balanced and Iterative Reducing and Clustering Using Hierarchies (BIRCH), PageRank, AdaBoost, support vector machines (SVM), Kohonen self-organizing maps (SOM), and latent semantic indexing (LSI);
more details on practical aspects and business understanding of a data-mining process, discussing important problems of validation, deployment, data understanding, causality, security, and privacy; and
some quantitative measures and methods for comparison of data-mining models such as ROC curve, lift chart, ROI chart, McNemar’s test, and K-fold cross validation paired t-test.

Keeping in mind the educational aspect of the book, many new exercises have been added. The bibliography and appendices have been updated to include work that has appeared in the last few years, as well as to reflect the change in emphasis when a new topic gained importance.

I would like to thank all my colleagues all over the world who used the first edition of the book for their classes and who sent me support, encouragement, and suggestions to put together this revised version. My sincere thanks are due to all my colleagues and students in the Data Mining Lab and Computer Science Department for their reviews of this edition, and numerous helpful suggestions. Special thanks go to graduate students Brent Wenerstrom, Chamila Walgampaya, and Wael Emara for patience in proofreading this new edition and for useful discussions about the content of new chapters, numerous corrections, and additions. To Dr. Joung Woo Ryu, who helped me enormously in the preparation of the final version of the text and all additional figures and tables, I would like to express my deepest gratitude.

I believe this book can serve as a valuable guide to the field for undergraduate, graduate students, researchers, and practitioners. I hope that the wide range of topics covered will allow readers to appreciate the extent of the impact of data mining on modern business, science, even the entire society.

MEHMED KANTARDZIC

Louisville

July 2011

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