Contents. Foreword to Second Edition. Acknowledgments About the Authors

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Miranda Hawkins
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1 Contents Foreword xix Foreword to Second Edition xxi Preface xxiii Acknowledgments About the Authors xxxi xxxv Chapter 1 Introduction Why Data Mining? Moving toward the Information Age Data Mining as the Evolution of Information Technology What Is Data Mining? What Kinds of Data Can Be Mined? Database Data Data Warehouses Transactional Data Other Kinds of Data What Kinds of Patterns Can Be Mined? Class/Concept Description: Characterization and Discrimination Mining Frequent Patterns, Associations, and Correlations Classification and Regression for Predictive Analysis Cluster Analysis Outlier Analysis Are All Patterns Interesting? Which Technologies Are Used? Statistics Machine Learning Database Systems and Data Warehouses Information Retrieval 26 ix

2 x Contents 1.6 Which Kinds of Applications Are Targeted? Business Intelligence Web Search Engines Major Issues in Data Mining Mining Methodology User Interaction Efficiency and Scalability Diversity of Database Types Data Mining and Society Summary Exercises Bibliographic Notes 35 Chapter 2 Getting to Know Your Data Data Objects and Attribute Types What Is an Attribute? Nominal Attributes Binary Attributes Ordinal Attributes Numeric Attributes Discrete versus Continuous Attributes Basic Statistical Descriptions of Data Measuring the Central Tendency: Mean, Median, and Mode Measuring the Dispersion of Data: Range, Quartiles, Variance, Standard Deviation, and Interquartile Range Graphic Displays of Basic Statistical Descriptions of Data Data Visualization Pixel-Oriented Visualization Techniques Geometric Projection Visualization Techniques Icon-Based Visualization Techniques Hierarchical Visualization Techniques Visualizing Complex Data and Relations Measuring Data Similarity and Dissimilarity Data Matrix versus Dissimilarity Matrix Proximity Measures for Nominal Attributes Proximity Measures for Binary Attributes Dissimilarity of Numeric Data: Minkowski Distance Proximity Measures for Ordinal Attributes Dissimilarity for Attributes of Mixed Types Cosine Similarity Summary Exercises Bibliographic Notes 81

3 Contents xi Chapter 3 Data Preprocessing Data Preprocessing: An Overview Data Quality: Why Preprocess the Data? Major Tasks in Data Preprocessing Data Cleaning Missing Values Noisy Data Data Cleaning as a Process Data Integration Entity Identification Problem Redundancy and Correlation Analysis Tuple Duplication Data Value Conflict Detection and Resolution Data Reduction Overview of Data Reduction Strategies Wavelet Transforms Principal Components Analysis Attribute Subset Selection Regression and Log-Linear Models: Parametric Data Reduction Histograms Clustering Sampling Data Cube Aggregation Data Transformation and Data Discretization Data Transformation Strategies Overview Data Transformation by Normalization Discretization by Binning Discretization by Histogram Analysis Discretization by Cluster, Decision Tree, and Correlation Analyses Concept Hierarchy Generation for Nominal Data Summary Exercises Bibliographic Notes 123 Chapter 4 Data Warehousing and Online Analytical Processing Data Warehouse: Basic Concepts What Is a Data Warehouse? Differences between Operational Database Systems and Data Warehouses But, Why Have a Separate Data Warehouse? 129

4 xii Contents Data Warehousing: A Multitiered Architecture Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse Extraction, Transformation, and Loading Metadata Repository Data Warehouse Modeling: Data Cube and OLAP Data Cube: A Multidimensional Data Model Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional Data Models Dimensions: The Role of Concept Hierarchies Measures: Their Categorization and Computation Typical OLAP Operations A Starnet Query Model for Querying Multidimensional Databases Data Warehouse Design and Usage A Business Analysis Framework for Data Warehouse Design Data Warehouse Design Process Data Warehouse Usage for Information Processing From Online Analytical Processing to Multidimensional Data Mining Data Warehouse Implementation Efficient Data Cube Computation: An Overview Indexing OLAP Data: Bitmap Index and Join Index Efficient Processing of OLAP Queries OLAP Server Architectures: ROLAP versus MOLAP versus HOLAP Data Generalization by Attribute-Oriented Induction Attribute-Oriented Induction for Data Characterization Efficient Implementation of Attribute-Oriented Induction Attribute-Oriented Induction for Class Comparisons Summary Exercises Bibliographic Notes 184 Chapter 5 Data Cube Technology Data Cube Computation: Preliminary Concepts Cube Materialization: Full Cube, Iceberg Cube, Closed Cube, and Cube Shell General Strategies for Data Cube Computation Data Cube Computation Methods Multiway Array Aggregation for Full Cube Computation 195

5 Contents xiii BUC: Computing Iceberg Cubes from the Apex Cuboid Downward Star-Cubing: Computing Iceberg Cubes Using a Dynamic Star-Tree Structure Precomputing Shell Fragments for Fast High-Dimensional OLAP Processing Advanced Kinds of Queries by Exploring Cube Technology Sampling Cubes: OLAP-Based Mining on Sampling Data Ranking Cubes: Efficient Computation of Top-k Queries Multidimensional Data Analysis in Cube Space Prediction Cubes: Prediction Mining in Cube Space Multifeature Cubes: Complex Aggregation at Multiple Granularities Exception-Based, Discovery-Driven Cube Space Exploration Summary Exercises Bibliographic Notes 240 Chapter 6 Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods Basic Concepts Market Basket Analysis: A Motivating Example Frequent Itemsets, Closed Itemsets, and Association Rules Frequent Itemset Mining Methods Apriori Algorithm: Finding Frequent Itemsets by Confined Candidate Generation Generating Association Rules from Frequent Itemsets Improving the Efficiency of Apriori A Pattern-Growth Approach for Mining Frequent Itemsets Mining Frequent Itemsets Using Vertical Data Format Mining Closed and Max Patterns Which Patterns Are Interesting? Pattern Evaluation Methods Strong Rules Are Not Necessarily Interesting From Association Analysis to Correlation Analysis A Comparison of Pattern Evaluation Measures Summary Exercises Bibliographic Notes 276

6 xiv Contents Chapter 7 Advanced Pattern Mining Pattern Mining: A Road Map Pattern Mining in Multilevel, Multidimensional Space Mining Multilevel Associations Mining Multidimensional Associations Mining Quantitative Association Rules Mining Rare Patterns and Negative Patterns Constraint-Based Frequent Pattern Mining Metarule-Guided Mining of Association Rules Constraint-Based Pattern Generation: Pruning Pattern Space and Pruning Data Space Mining High-Dimensional Data and Colossal Patterns Mining Colossal Patterns by Pattern-Fusion Mining Compressed or Approximate Patterns Mining Compressed Patterns by Pattern Clustering Extracting Redundancy-Aware Top-k Patterns Pattern Exploration and Application Semantic Annotation of Frequent Patterns Applications of Pattern Mining Summary Exercises Bibliographic Notes 323 Chapter 8 Classification: Basic Concepts Basic Concepts What Is Classification? General Approach to Classification Decision Tree Induction Decision Tree Induction Attribute Selection Measures Tree Pruning Scalability and Decision Tree Induction Visual Mining for Decision Tree Induction Bayes Classification Methods Bayes Theorem Naïve Bayesian Classification Rule-Based Classification Using IF-THEN Rules for Classification Rule Extraction from a Decision Tree Rule Induction Using a Sequential Covering Algorithm 359

7 Contents xv 8.5 Model Evaluation and Selection Metrics for Evaluating Classifier Performance Holdout Method and Random Subsampling Cross-Validation Bootstrap Model Selection Using Statistical Tests of Significance Comparing Classifiers Based on Cost Benefit and ROC Curves Techniques to Improve Classification Accuracy Introducing Ensemble Methods Bagging Boosting and AdaBoost Random Forests Improving Classification Accuracy of Class-Imbalanced Data Summary Exercises Bibliographic Notes 389 Chapter 9 Classification: Advanced Methods Bayesian Belief Networks Concepts and Mechanisms Training Bayesian Belief Networks Classification by Backpropagation A Multilayer Feed-Forward Neural Network Defining a Network Topology Backpropagation Inside the Black Box: Backpropagation and Interpretability Support Vector Machines The Case When the Data Are Linearly Separable The Case When the Data Are Linearly Inseparable Classification Using Frequent Patterns Associative Classification Discriminative Frequent Pattern Based Classification Lazy Learners (or Learning from Your Neighbors) k-nearest-neighbor Classifiers Case-Based Reasoning Other Classification Methods Genetic Algorithms Rough Set Approach Fuzzy Set Approaches Additional Topics Regarding Classification Multiclass Classification 430

8 xvi Contents Semi-Supervised Classification Active Learning Transfer Learning Summary Exercises Bibliographic Notes 439 Chapter 10 Cluster Analysis: Basic Concepts and Methods Cluster Analysis What Is Cluster Analysis? Requirements for Cluster Analysis Overview of Basic Clustering Methods Partitioning Methods k-means: A Centroid-Based Technique k-medoids: A Representative Object-Based Technique Hierarchical Methods Agglomerative versus Divisive Hierarchical Clustering Distance Measures in Algorithmic Methods BIRCH: Multiphase Hierarchical Clustering Using Clustering Feature Trees Chameleon: Multiphase Hierarchical Clustering Using Dynamic Modeling Probabilistic Hierarchical Clustering Density-Based Methods DBSCAN: Density-Based Clustering Based on Connected Regions with High Density OPTICS: Ordering Points to Identify the Clustering Structure DENCLUE: Clustering Based on Density Distribution Functions Grid-Based Methods STING: STatistical INformation Grid CLIQUE: An Apriori-like Subspace Clustering Method Evaluation of Clustering Assessing Clustering Tendency Determining the Number of Clusters Measuring Clustering Quality Summary Exercises Bibliographic Notes 494 Chapter 11 Advanced Cluster Analysis Probabilistic Model-Based Clustering Fuzzy Clusters 499

9 Contents xvii Probabilistic Model-Based Clusters Expectation-Maximization Algorithm Clustering High-Dimensional Data Clustering High-Dimensional Data: Problems, Challenges, and Major Methodologies Subspace Clustering Methods Biclustering Dimensionality Reduction Methods and Spectral Clustering Clustering Graph and Network Data Applications and Challenges Similarity Measures Graph Clustering Methods Clustering with Constraints Categorization of Constraints Methods for Clustering with Constraints Summary Exercises Bibliographic Notes 540 Chapter 12 Outlier Detection Outliers and Outlier Analysis What Are Outliers? Types of Outliers Challenges of Outlier Detection Outlier Detection Methods Supervised, Semi-Supervised, and Unsupervised Methods Statistical Methods, Proximity-Based Methods, and Clustering-Based Methods Statistical Approaches Parametric Methods Nonparametric Methods Proximity-Based Approaches Distance-Based Outlier Detection and a Nested Loop Method A Grid-Based Method Density-Based Outlier Detection Clustering-Based Approaches Classification-Based Approaches Mining Contextual and Collective Outliers Transforming Contextual Outlier Detection to Conventional Outlier Detection 573

10 xviii Contents Modeling Normal Behavior with Respect to Contexts Mining Collective Outliers Outlier Detection in High-Dimensional Data Extending Conventional Outlier Detection Finding Outliers in Subspaces Modeling High-Dimensional Outliers Summary Exercises Bibliographic Notes 583 Chapter 13 Data Mining Trends and Research Frontiers Mining Complex Data Types Mining Sequence Data: Time-Series, Symbolic Sequences, and Biological Sequences Mining Graphs and Networks Mining Other Kinds of Data Other Methodologies of Data Mining Statistical Data Mining Views on Data Mining Foundations Visual and Audio Data Mining Data Mining Applications Data Mining for Financial Data Analysis Data Mining for Retail and Telecommunication Industries Data Mining in Science and Engineering Data Mining for Intrusion Detection and Prevention Data Mining and Recommender Systems Data Mining and Society Ubiquitous and Invisible Data Mining Privacy, Security, and Social Impacts of Data Mining Data Mining Trends Summary Exercises Bibliographic Notes 628 Bibliography 633 Index 673

Table Of Contents: xix Foreword to Second Edition

Table Of Contents: xix Foreword to Second Edition Data Mining : Concepts and Techniques Table Of Contents: Foreword xix Foreword to Second Edition xxi Preface xxiii Acknowledgments xxxi About the Authors xxxv Chapter 1 Introduction 1 (38) 1.1 Why Data