Data Mining - Know It All (Elsevier, 2009)

Contents
About This Book ........................................................................... ix
Contributing Authors .................................................................... xi
CHAPTER 1 What’s It All About? ...................................................... 1
1.1 Data Mining and Machine Learning ................................... 1
1.2 Simple Examples: The Weather Problem
and Others ......................................................................... 7
1.3 Fielded Applications .......................................................... 20
1.4 Machine Learning and Statistics ........................................ 27
1.5 Generalization as Search .................................................... 28
1.6 Data Mining and Ethics ...................................................... 32
1.7 Resources ........................................................................... 34
CHAPTER 2 Data Acquisition and Integration ............................... 37
2.1 Introduction ....................................................................... 37
2.2 Sources of Data .................................................................. 37
2.3 Variable Types.................................................................... 39
2.4 Data Rollup ........................................................................ 41
2.5 Rollup with Sums, Averages, and Counts ......................... 48
2.6 Calculation of the Mode .................................................... 49
2.7 Data Integration ................................................................. 50
CHAPTER 3 Data Preprocessing ....................................................... 57
3.1 Why Preprocess the Data? ................................................. 58
3.2 Descriptive Data Summarization ....................................... 61
3.3 Data Cleaning ..................................................................... 72
3.4 Data Integration and Transformation ................................ 78
3.5 Data Reduction .................................................................. 84
3.6 Data Discretization and Concept
Hierarchy Generation ........................................................ 98
3.7 Summary ............................................................................ 108
3.8 Resources ........................................................................... 109
CHAPTER 4 Physical Design for Decision Support,
Warehousing, and OLAP .............................................. 113
4.1 What Is Online Analytical Processing? .............................. 113
4.2 Dimension Hierarchies ...................................................... 116
4.3 Star and Snowflake Schemas ............................................. 117
4.4 Warehouses and Marts ....................................................... 119
4.5 Scaling Up the System ....................................................... 122
4.6 DSS, Warehousing, and OLAP
Design Considerations ....................................................... 124
4.7 Usage Syntax and Examples for Major
Database Servers ................................................................ 125
4.8 Summary ............................................................................ 128
4.9 Literature Summary ............................................................ 129
Resources ........................................................................... 129
CHAPTER 5 Algorithms: The Basic Methods ................................. 131
5.1 Inferring Rudimentary Rules .............................................. 132
5.2 Statistical Modeling ............................................................ 136
5.3 Divide and Conquer: Constructing Decision Trees .......... 144
5.4 Covering Algorithms: Constructing Rules ......................... 153
5.5 Mining Association Rules ................................................... 160
5.6 Linear Models ..................................................................... 168
5.7 Instance-Based Learning ..................................................... 176
5.8 Clustering ........................................................................... 184
5.9 Resources ........................................................................... 188
CHAPTER 6 Further Techniques in Decision Analysis ................ 191
6.1 Modeling Risk Preferences ................................................ 191
6.2 Analyzing Risk Directly ...................................................... 198
6.3 Dominance ......................................................................... 200
6.4 Sensitivity Analysis ............................................................. 205
6.5 Value of Information .......................................................... 215
6.6 Normative Decision Analysis ............................................. 220
CHAPTER 7 Fundamental Concepts of
Genetic Algorithms ........................................................ 221
7.1 The Vocabulary of Genetic Algorithms ............................. 222
7.2 Overview ............................................................................ 230
7.3 The Architecture of a Genetic Algorithm ......................... 241
7.4 Practical Issues in Using a Genetic Algorithm .................. 285
7.5 Review ............................................................................... 290
7.6 Resources ........................................................................... 290
CHAPTER 8 Data Structures and Algorithms for Moving
Objects Types ................................................................. 293
8.1 Data Structures ................................................................... 293
8.2 Algorithms for Operations on Temporal
Data Types ......................................................................... 298
8.3 Algorithms for Lifted Operations ....................................... 310
8.4 Resources ........................................................................... 319
CHAPTER 9 Improving the Model ..................................................... 321
9.1 Learning from Errors .......................................................... 323
9.2 Improving Model Quality, Solving Problems .................... 343
9.3 Summary ............................................................................ 395
CHAPTER 10 Social Network Analysis .............................................. 397
10.1 Social Sciences and Bibliometry ........................................ 398
10.2 PageRank and Hyperlink-Induced Topic Search ............... 400
10.3 Shortcomings of the Coarse-Grained Graph Model .......... 410
10.4 Enhanced Models and Techniques .................................... 416
10.5 Evaluation of Topic Distillation ......................................... 424
10.6 Measuring and Modeling the Web .................................... 430
10.7 Resources ........................................................................... 440
Index ........................................................................................... 443

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