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首页数据挖掘与分析:基本概念与算法 英文版 DATA MINING AND ANALYSIS Fundamental Concepts and Algorithms
The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.
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D A T A M I N I N G A N D A N A L Y S I S
The fundamental algorithms in data mining and analysis form the basis
for the emerging field of data science, which includes automated methods
to analyze patterns and models for all kinds of data, with applications
ranging from scientific discovery to business intelligence and analytics.
This textbook for senior undergraduate and graduate d ata mining courses
provides a broad yet i n -depth overview of data mining, integrating related
concepts from machine learning and statistics. Th e main parts of the
book include exploratory data analysis, pattern mining, clustering, and
classification. The book lays the basic foundations of these tasks and
also covers cutting-edge topics such as kernel methods, high-dimensional
data analysis, and complex graphs and networks. With its comprehensive
coverage, algorithmic perspective, and wealth of examples, this book
offers solid guidance in data mining for students, researchers, and
practitioners alike.
Key Features:
•
Covers both core methods and cutting-edge research
•
Algorithmic approach with open-source implementations
•
Minimal prerequisites, as all key mathematical concepts are
presented, as is the intuition behind the formulas
•
Short, self-contained chapters with class-tested examples and
exercises that allow for fl exibility in designing a cours e and for easy
reference
•
Supplementary online resource containing lecture slides, videos,
project ideas, and more
Mohammed J. Zaki is a Professor of Computer Science at Rensselaer
Polytechnic Institute, Troy, New York.
Wagner Meira Jr. is a Professor of Computer Science at Universidade
Federal de Minas Gerais, Brazil.
DATA MINING
AND ANALYSIS
Fundamental Concepts and Algorithms
MOHAMMED J. ZAKI
Rensselaer Polytechnic Institute, Troy, New York
WAGNER MEIRA JR.
Universidade Federal de Minas Gerais, Brazil
32 Avenue o f the Americas, New York, NY 10013-2473, USA
Cambridge University Press is part of the University of Cambridge.
It furthers the University’s mission by disseminating kno w ledge in the pursuit of
education, learning, and research at the highest international levels of excellence.
www.cambridge.org
Information on this title: www.cambridge.org/9780521766333
c
Mohammed J. Zaki and Wagner Meira Jr. 2014
This publication is in copyright. Su b ject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the wr itten
permission of Cambridge University Press.
First published 2014
Printed in the United States of America
A catalog record for this publication is available from the British Library.
Library of Congress Cataloging in Publication Data
Zaki, Mohammed J., 1971–
Data mining and analysis: fundamental concepts and algorithms / Mohammed J. Zaki,
Rensselaer Polytechnic Institute, Troy, New York , Wagner M eira Jr.,
Universidade Federal de Minas Gerais, Brazil.
pages cm
Includes bibliographical references and index.
ISBN 978-0-521-76633-3 (hardback)
1. Data mining. I. Meira, Wagner, 1967– II. Title.
QA76.9.D343Z36 2014
006.3
′
12–dc23 2013037544
ISBN 978-0-521-76633-3 Hardback
Cambridge University Press has no responsibility for the persistence or accuracy of
URLs for external or third-party Internet Web sites referred to in this publication
and does not guarantee that any content on such Web sites is, or will remain,
accurate or appropriate.
Contents
Preface page ix
1 Data Mining and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Data Matrix 1
1.2 Attributes 3
1.3 Data: Algebraic and Geometric View 4
1.4 Data: Probabilistic View 14
1.5 Data Mining 25
1.6 Further Reading 30
1.7 Exercises 30
PART ONE: DATA ANALYSIS FOUNDATIONS
2 Numeric Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1 Univariate Analysis 33
2.2 Bivariate Analysis 42
2.3 Multivariate Analysis 48
2.4 Data Normalization 52
2.5 Normal Distributi on 54
2.6 Further Reading 60
2.7 Exercises 60
3 Categorical Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.1 Univariate Analysis 63
3.2 Bivariate Analysis 72
3.3 Multivariate Analysis 82
3.4 Distance and Angle 87
3.5 Discretization 89
3.6 Further Reading 91
3.7 Exercises 91
4 Graph Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.1 Graph Concepts 93
4.2 Topological Attributes 97
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