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Foreword vii
Preface xix
Introduction 1 (38)
What Motivated Data Mining? Why Is It 1 (4)
Important?
So, What Is Data Mining? 5 (5)
Data Mining---On What Kind of Data? 10 (11)
Relational Databases 10 (2)
Data Warehouses 12 (3)
Transactional Databases 15 (1)
Advanced Database Systems and Advanced 16 (5)
Database Applications
Data Mining Functionalities---What Kinds of 21 (6)
Patterns Can Be Mined?
Concept/Class Description: 21 (2)
Characterization and Discrimination
Association Analysis 23 (1)
Classification and Prediction 24 (1)
Cluster Analysis 25 (1)
Outlier Analysis 25 (1)
Evolution Analysis 26 (1)
Are All of the Patterns Interesting? 27 (1)
Classification of Data Mining Systems 28 (2)
Major Issues in Data Mining 30 (3)
Summary 33 (6)
Exercises 34 (1)
Bibliographic Notes 35 (4)
Data Warehouse and OLAP Technology for Data 39 (66)
Mining
What Is a Data Warehouse? 39 (5)
Differences between Operational Database 42 (2)
Systems and Data Warehouses
But, Why Have a Separate Data Warehouse? 44 (1)
A Multidimensional Data Model 44 (18)
From Tables and Spreadsheets to Data Cubes 45 (3)
Stars, Snowflakes, and Fact 48 (4)
Constellations: Schemas for
Multidimensional Databases
Examples for Defining Star, Snowflake, 52 (2)
and Fact Constellation Schemas
Measures: Their Categorization and 54 (2)
Computation
Introducing Concept Hierarchies 56 (2)
OLAP Operations in the Multidimensional 58 (3)
Data Model
A Starnet Query Model for Querying 61 (1)
Multidimensional Databases
Data Warehouse Architecture 62 (9)
Steps for the Design and Construction of 63 (2)
Data Warehouses
A Three-Tier Data Warehouse Architecture 65 (4)
Types of OLAP Servers: ROLAP versus MOLAP 69 (2)
versus HOLAP
Data Warehouse Implementation 71 (14)
Efficient Computation of Data Cubes 71 (8)
Indexing OLAP Data 79 (2)
Efficient Processing of OLAP Queries 81 (2)
Metadata Repository 83 (1)
Data Warehouse Back-End Tools and 84 (1)
Utilities
Further Development of Data Cube Technology 85 (8)
Discovery-Driven Exploration of Data Cubes 85 (4)
Complex Aggregation at Multiple 89 (3)
Granularities: Multifeature Cubes
Other Developments 92 (1)
From Data Warehousing to Data Mining 93 (5)
Data Warehouse Usage 93 (2)
From On-Line Analytical Processing to 95 (3)
On-Line Analytical Mining
Summary 98 (7)
Exercises 99 (4)
Bibliographic Notes 103(2)
Data Preprocessing 105(40)
Why Preprocess the Data? 105(4)
Data Cleaning 109(3)
Missing Values 109(1)
Noisy Data 110(2)
Inconsistent Data 112(1)
Data Integration and Transformation 112(4)
Data Integration 112(2)
Data Transformation 114(2)
Data Reduction 116(14)
Data Cube Aggregation 117(2)
Dimensionality Reduction 119(2)
Data Compression 121(3)
Numerosity Reduction 124(6)
Discretization and Concept Hierarchy 130(10)
Generation
Discretization and Concept Hierarchy 132(6)
Generation for Numeric Data
Concept Hierarchy Generation for 138(2)
Categorical Data
Summary 140(5)
Exercises 141(1)
Bibliographic Notes 142(3)
Data Mining Primitives, Languages, and System 145(34)
Architectures
Data Mining Primitives: What Defines a Data 146(13)
Mining Task?
Task-Relevant Data 148(2)
The Kind of Knowledge to be Mined 150(1)
Background Knowledge: Concept Hierarchies 151(4)
Interestingness Measures 155(2)
Presentation and Visualization of 157(2)
Discovered Patterns
A Data Mining Query Language 159(11)
Syntax for Task-Relevant Data 160(2)
Specification
Syntax for Specifying the Kind of 162(3)
Knowledge to be Mined
Syntax for Concept Hierarchy Specification 165(1)
Syntax for Interestingness Measure 166(1)
Specification
Syntax for Pattern Presentation and 167(1)
Visualization Specification
Putting It All Together---An Example of a 167(2)
DMQL Query
Other Data Mining Languages and the 169(1)
Standardization of Data Mining Primitives
Designing Graphical User Interfaces Based 170(1)
on a Data Mining Query Language
Architectures of Data Mining Systems 171(3)
Summary 174(5)
Exercises 174(2)
Bibliographic Notes 176(3)
Concept Description: Characterization and 179(46)
Comparison
What Is Concept Description? 179(2)
Data Generalization and Summarization-Based 181(13)
Characterization
Attribute-Oriented Induction 182(5)
Efficient Implementation of 187(3)
Attribute-Oriented Induction
Presentation of the Derived Generalization 190(4)
Analytical Characterization: Analysis of 194(6)
Attribute Relevance
Why Perform Attribute Relevance Analysis? 195(1)
Methods of Attribute Relevance Analysis 196(2)
Analytical Characterization: An Example 198(2)
Mining Class Comparisons: Discriminating 200(8)
between Different Classes
Class Comparison Methods and 201(3)
Implementations
Presentation of Class Comparison 204(2)
Descriptions
Class Description: Presentation of Both 206(2)
Characterization and Comparison
Mining Descriptive Statistical Measures in 208(9)
Large Databases
Measuring the Central Tendency 209(1)
Measuring the Dispersion of Data 210(3)
Graph Displays of Basic Statistical Class 213(4)
Descriptions
Discussion 217(3)
Concept Description: A Comparison with 218(2)
Typical Machine Learning Methods
Incremental and Parallel Mining of 220(1)
Concept Description
Summary 220(5)
Exercises 222(1)
Bibliographic Notes 223(2)
Mining Association Rules in Large Databases 225(54)
Association Rule Mining 226(4)
Market Basket Analysis: A Motivating 226(1)
Example for Association Rule Mining
Basic Concepts 227(2)
Association Rule Mining: A Road Map 229(1)
Mining Single-Dimensional Boolean 230(14)
Association Rules from Transactional
Databases
The Apriori Algorithm: Finding Frequent 230(6)
Itemsets Using Candidate Generation
Generating Association Rules from 236(1)
Frequent Itemsets
Improving the Efficiency of Apriori 236(3)
Mining Frequent Itemsets without 239(4)
Candidate Generation
Iceberg Queries 243(1)
Mining Multilevel Association Rules from 244(7)
Transaction Databases
Multilevel Association Rules 244(2)
Approaches to Mining Multilevel 246(4)
Association Rules
Checking for Redundant Multilevel 250(1)
Association Rules
Mining Multidimensional Association Rules 251(8)
from Relational Databases and Data
Warehouses
Multidimensional Association Rules 251(2)
Mining Multidimensional Association Rules 253(1)
Using Static Discretization of
Quantitative Attributes
Mining Quantitative Association Rules 254(3)
Mining Distance-Based Association Rules 257(2)
From Association Mining to Correlation 259(3)
Analysis
Strong Rules Are Not Necessarily 259(1)
Interesting: An Example
From Association Analysis to Correlation 260(2)
Analysis
Constraint-Based Association Mining 262(7)
Metarule-Guided Mining of Association 263(2)
Rules
Mining Guided by Additional Rule 265(4)
Constraints
Summary 269(10)
Exercises 271(5)
Bibliographic Notes 276(3)
Classification and Prediction 279(56)
What Is Classification? What Is Prediction? 279(3)
Issues Regarding Classification and 282(2)
Prediction
Preparing the Data for Classification and 282(1)
Prediction
Comparing Classification Methods 283(1)
Classification by Decision Tree Induction 284(12)
Decision Tree Induction 285(4)
Tree Pruning 289(1)
Extracting Classification Rules from 290(1)
Decision Trees
Enhancements to Basic Decision Tree 291(1)
Induction
Scalability and Decision Tree Induction 292(2)
Integrating Data Warehousing Techniques 294(2)
and Decision Tree Induction
Bayesian Classification 296(7)
Bayes Theorem 296(1)
Naive Bayesian Classification 297(2)
Bayesian Belief Networks 299(2)
Training Bayesian Belief Networks 301(2)
Classification by Backpropagation 303(8)
A Multilayer Feed-Forward Neural Network 303(1)
Defining a Network Topology 304(1)
Backpropagation 305(5)
Backpropagation and Interpretability 310(1)
Classification Based on Concepts from 311(3)
Association Rule Mining
Other Classification Methods 314(5)
k-Nearest Neighbor Classifiers 314(1)
Case-Based Reasoning 315(1)
Genetic Algorithms 316(1)
Rough Set Approach 316(1)
Fuzzy Set Approaches 317(2)
Prediction 319(3)
Linear and Multiple Regression 319(2)
Nonlinear Regression 321(1)
Other Regression Models 322(1)
Classifier Accuracy 322(4)
Estimating Classifier Accuracy 323(1)
Increasing Classifier Accuracy 324(1)
Is Accuracy Enough to Judge a Classifier? 325(1)
Summary 326(9)
Exercises 328(2)
Bibliographic Notes 330(5)
Cluster Analysis 335(60)
What Is Cluster Analysis? 335(3)
Types of Data in Cluster Analysis 338(8)
Interval-Scaled Variables 339(2)
Binary Variables 341(2)
Nominal, Ordinal, and Ratio-Scaled 343(2)
Variables
Variables of Mixed Types 345(1)
A Categorization of Major Clustering Methods 346(2)
Partitioning Methods 348(6)
Classical Partitioning Methods: k-Means 349(4)
and k-Medoids
Partitioning Methods in Large Databases: 353(1)
From k-Medoids to CLARANS
Hierarchical Methods 354(9)
Agglomerative and Divisive Hierarchical 355(2)
Clustering
BIRCH: Balanced Iterative Reducing and 357(1)
Clustering Using Hierarchies
CURE: Clustering Using REpresentatives 358(3)
Chameleon: A Hierarchical Clustering 361(2)
Algorithm Using Dynamic Modeling
Density-Based Methods 363(7)
DBSCAN: A Density-Based Clustering Method 363(2)
Based on Connected Regions with
Sufficiently High Density
OPTICS: Ordering Points to Identify the 365(1)
Clustering Structure
DENCLUE: Clustering Based on Density 366(4)
Distribution Functions
Grid-Based Methods 370(6)
STING: STatistical INformation Grid 370(2)
WaveCluster: Clustering Using Wavelet 372(2)
Transformation
CLIQUE: Clustering High-Dimensional Space 374(2)
Model-Based Clustering Methods 376(5)
Statistical Approach 376(3)
Neural Network Approach 379(2)
Outlier Analysis 381(7)
Statistical-Based Outlier Detection 382(2)
Distance-Based Outlier Detection 384(2)
Deviation-Based Outlier Detection