Table of Contents
Cover
Series page
Title page
Copyright page
DEDICATION
PREFACE TO THE SECOND EDITION
PREFACE TO THE FIRST EDITION
1 DATA-MINING CONCEPTS
1.1 INTRODUCTION
1.2 DATA-MINING ROOTS
1.3 DATA-MINING PROCESS
1.4 LARGE DATA SETS
1.5 DATA WAREHOUSES FOR DATA MINING
1.6 BUSINESS ASPECTS OF DATA MINING: WHY A DATA-MINING PROJECT FAILS
1.7 ORGANIZATION OF THIS BOOK
2 PREPARING THE DATA
2.1 REPRESENTATION OF RAW DATA
2.2 CHARACTERISTICS OF RAW DATA
2.3 TRANSFORMATION OF RAW DATA
2.4 MISSING DATA
2.5 TIME-DEPENDENT DATA
2.6 OUTLIER ANALYSIS
3 DATA REDUCTION
3.1 DIMENSIONS OF LARGE DATA SETS
3.2 FEATURE REDUCTION
3.3 RELIEF ALGORITHM
3.4 ENTROPY MEASURE FOR RANKING FEATURES
3.5 PCA
3.6 VALUE REDUCTION
3.7 FEATURE DISCRETIZATION: CHIMERGE TECHNIQUE
3.8 CASE REDUCTION
4 LEARNING FROM DATA
4.1 LEARNING MACHINE
4.2 SLT
4.3 TYPES OF LEARNING METHODS
4.4 COMMON LEARNING TASKS
4.5 SVMs
4.6 KNN: NEAREST NEIGHBOR CLASSIFIER
4.7 MODEL SELECTION VERSUS GENERALIZATION
4.8 MODEL ESTIMATION
4.9 90% ACCURACY: NOW WHAT?
5 STATISTICAL METHODS
5.1 STATISTICAL INFERENCE
5.2 ASSESSING DIFFERENCES IN DATA SETS
5.3 BAYESIAN INFERENCE
5.4 PREDICTIVE REGRESSION
5.5 ANOVA
5.6 LOGISTIC REGRESSION
5.7 LOG-LINEAR MODELS
5.8 LDA
6 DECISION TREES AND DECISION RULES
6.1 DECISION TREES
6.2 C4.5 ALGORITHM: GENERATING A DECISION TREE
6.3 UNKNOWN ATTRIBUTE VALUES
6.4 PRUNING DECISION TREES
6.5 C4.5 ALGORITHM: GENERATING DECISION RULES
6.6 CART ALGORITHM & GINI INDEX
6.7 LIMITATIONS OF DECISION TREES AND DECISION RULES
7 ARTIFICIAL NEURAL NETWORKS
7.1 MODEL OF AN ARTIFICIAL NEURON
7.2 ARCHITECTURES OF ANNS
7.3 LEARNING PROCESS
7.4 LEARNING TASKS USING ANNS
7.5 MULTILAYER PERCEPTRONS (MLPs)
7.6 COMPETITIVE NETWORKS AND COMPETITIVE LEARNING
7.7 SOMs
8 ENSEMBLE LEARNING
8.1 ENSEMBLE-LEARNING METHODOLOGIES
8.2 COMBINATION SCHEMES FOR MULTIPLE LEARNERS
8.3 BAGGING AND BOOSTING
8.4 ADABOOST
9 CLUSTER ANALYSIS
9.1 CLUSTERING CONCEPTS
9.2 SIMILARITY MEASURES
9.3 AGGLOMERATIVE HIERARCHICAL CLUSTERING
9.4 PARTITIONAL CLUSTERING
9.5 INCREMENTAL CLUSTERING
9.6 DBSCAN ALGORITHM
9.7 BIRCH ALGORITHM
9.8 CLUSTERING VALIDATION
10 ASSOCIATION RULES
10.1 MARKET-BASKET ANALYSIS
10.2 ALGORITHM APRIORI
10.3 FROM FREQUENT ITEMSETS TO ASSOCIATION RULES
10.4 IMPROVING THE EFFICIENCY OF THE APRIORI ALGORITHM
10.5 FP GROWTH METHOD
10.6 ASSOCIATIVE-CLASSIFICATION METHOD
10.7 MULTIDIMENSIONAL ASSOCIATION–RULES MINING
11 WEB MINING AND TEXT MINING
11.1 WEB MINING
11.2 WEB CONTENT, STRUCTURE, AND USAGE MINING
11.3 HITS AND LOGSOM ALGORITHMS
11.4 MINING PATH–TRAVERSAL PATTERNS
11.5 PAGERANK ALGORITHM
11.6 TEXT MINING
11.7 LATENT SEMANTIC ANALYSIS (LSA)
12 ADVANCES IN DATA MINING
12.1 GRAPH MINING
12.2 TEMPORAL DATA MINING
12.3 SPATIAL DATA MINING (SDM)
12.4 DISTRIBUTED DATA MINING (DDM)
12.5 CORRELATION DOES NOT IMPLY CAUSALITY
12.6 PRIVACY, SECURITY, AND LEGAL ASPECTS OF DATA MINING
13 GENETIC ALGORITHMS
13.1 FUNDAMENTALS OF GAs
13.2 OPTIMIZATION USING GAs
13.3 A SIMPLE ILLUSTRATION OF A GA
13.4 SCHEMATA
13.5 TSP
13.6 MACHINE LEARNING USING GAs
13.7 GAS FOR CLUSTERING
14 FUZZY SETS AND FUZZY LOGIC
14.1 FUZZY SETS
14.2 FUZZY-SET OPERATIONS
14.3 EXTENSION PRINCIPLE AND FUZZY RELATIONS
14.4 FUZZY LOGIC AND FUZZY INFERENCE SYSTEMS
14.5 MULTIFACTORIAL EVALUATION
14.6 EXTRACTING FUZZY MODELS FROM DATA
14.7 DATA MINING AND FUZZY SETS
15 VISUALIZATION METHODS
15.1 PERCEPTION AND VISUALIZATION
15.2 SCIENTIFIC VISUALIZATION AND INFORMATION VISUALIZATION
15.3 PARALLEL COORDINATES
15.4 RADIAL VISUALIZATION
15.5 VISUALIZATION USING SELF-ORGANIZING MAPS (SOMs)
15.6 VISUALIZATION SYSTEMS FOR DATA MINING
APPENDIX A
A.1 DATA-MINING JOURNALS
A.2 DATA-MINING CONFERENCES
A.3 DATA-MINING FORUMS/BLOGS
A.4 DATA SETS
A.5 COMERCIALLY AND PUBLICLY AVAILABLE TOOLS
A.6 WEB SITE LINKS
APPENDIX B: DATA-MINING APPLICATIONS
B.1 DATA MINING FOR FINANCIAL DATA ANALYSIS
B.2 DATA MINING FOR THE TELECOMUNICATIONS INDUSTRY
B.3 DATA MINING FOR THE RETAIL INDUSTRY
B.4 DATA MINING IN HEALTH CARE AND BIOMEDICAL RESEARCH
B.5 DATA MINING IN SCIENCE AND ENGINEERING
B.6 PITFALLS OF DATA MINING
BIBLIOGRAPHY
Index
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University of Alberta
Edmonton, Alberta, Canada
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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
