Efnisyfirlit

• Title Page
• Copyright Page
• Dedication
• Preface to the Second Edition
• Contents
• 1 Introduction
  • 1.1 What Is Data Mining?
  • 1.2 Motivating Challenges
  • 1.3 the Origins of Data Mining
  • 1.4 Data Mining Tasks
  • 1.5 Scope and Organization of the Book
  • 1.6 Bibliographic Notes
  • 1.7 Exercises
• 2 Data
  • 2.1 Types of Data
    • 2.1.1 Attributes and Measurement
    • 2.1.2 Types of Data Sets
  • 2.2 Data Quality
    • 2.2.1 Measurement and Data Collection Issues
    • 2.2.2 Issues Related to Applications
  • 2.3 Data Preprocessing
    • 2.3.1 Aggregation
    • 2.3.2 Sampling
    • 2.3.3 Dimensionality Reduction
    • 2.3.4 Feature Subset Selection
    • 2.3.5 Feature Creation
    • 2.3.6 Discretization and Binarization
    • 2.3.7 Variable Transformation
  • 2.4 Measures of Similarity and Dissimilarity
    • 2.4.1 Basics
    • 2.4.2 Similarity and Dissimilarity Between Simple Attributes
    • 2.4.3 Dissimilarities Between Data Objects
    • 2.4.4 Similarities Between Data Objects
    • 2.4.5 Examples of Proximity Measures
    • 2.4.6 Mutual Information
    • 2.4.7 Kernel Functions*
    • 2.4.8 Bregman Divergence*
    • 2.4.9 Issues in Proximity Calculation
    • 2.4.10 Selecting the Right Proximity Measure
  • 2.5 Bibliographic Notes
  • 2.6 Exercises
• 3 Classification: Basic Concepts and Techniques
  • 3.1 Basic Concepts
  • 3.2 General Framework for Classification
  • 3.3 Decision Tree Classifier
    • 3.3.1 A Basic Algorithm to Build a Decision Tree
    • 3.3.2 Methods for Expressing Attribute Test Conditions
    • 3.3.3 Measures for Selecting an Attribute Test Condition
    • 3.3.4 Algorithm for Decision Tree Induction
    • 3.3.5 Example Application: Web Robot Detection
    • 3.3.6 Characteristics of Decision Tree Classifiers
  • 3.4 Model Overfitting
    • 3.4.1 Reasons for Model Overfitting
  • 3.5 Model Selection
    • 3.5.1 Using a Validation Set
    • 3.5.2 Incorporating Model Complexity
    • 3.5.3 Estimating Statistical Bounds
    • 3.5.4 Model Selection for Decision Trees
  • 3.6 Model Evaluation
    • 3.6.1 Holdout Method
    • 3.6.2 Cross-validation
  • 3.7 Presence of Hyper-parameters
    • 3.7.1 Hyper-parameter Selection
    • 3.7.2 Nested Cross-validation
  • 3.8 Pitfalls of Model Selection and Evaluation
    • 3.8.1 Overlap Between Training and Test Sets
    • 3.8.2 Use of Validation Error as Generalization Error
  • 3.9 Model Comparison*
    • 3.9.1 Estimating the Confidence Interval for Accuracy
    • 3.9.2 Comparing the Performance of Two Models
  • 3.10 Bibliographic Notes
  • 3.11 Exercises
• 4 Association Analysis: Basic Concepts and Algorithms
  • 4.1 Preliminaries
  • 4.2 Frequent Itemset Generation
    • 4.2.1 The Apriori Principle
    • 4.2.2 Frequent Itemset Generation in the Algorithm
    • 4.2.3 Candidate Generation and Pruning
    • 4.2.4 Support Counting
    • 4.2.5 Computational Complexity
  • 4.3 Rule Generation
    • 4.3.1 Confidence-based Pruning
    • 4.3.2 Rule Generation in Algorithm
    • 4.3.3 an Example: Congressional Voting Records
  • 4.4 Compact Representation of Frequent Itemsets
    • 4.4.1 Maximal Frequent Itemsets
    • 4.4.2 Closed Itemsets
  • 4.5 Alternative Methods for Generating Frequent Itemsets*
  • 4.6 FP-Growth Algorithm*
    • 4.6.1 FP-Tree Representation
    • 4.6.2 Frequent Itemset Generation in FP-Growth Algorithm
  • 4.7 Evaluation of Association Patterns
    • 4.7.1 Objective Measures of Interestingness
    • 4.7.2 Measures Beyond Pairs of Binary Variables
    • 4.7.3 Simpson’s Paradox
  • 4.8 Effect of Skewed Support Distribution
  • 4.9 Bibliographic Notes
  • 4.10 Exercises
• 5 Cluster Analysis: Basic Concepts and Algorithms
  • 5.1 Overview
    • 5.1.1 What Is Cluster Analysis?
    • 5.1.2 Different Types of Clusterings
    • 5.1.3 Different Types of Clusters
    • Road Map
  • 5.2 K-means
    • 5.2.1 The Basic K-means Algorithm
    • 5.2.2 K-means: Additional Issues
    • 5.2.3 Bisecting K-means
    • 5.2.4 K-means and Different Types of Clusters
    • 5.2.5 Strengths and Weaknesses
    • 5.2.6 K-means as an Optimization Problem
  • 5.3 Agglomerative Hierarchical Clustering
    • 5.3.1 Basic Agglomerative Hierarchical Clustering Algorithm
    • 5.3.2 Specific Techniques
    • 5.3.3 The Lance-williams Formula for Cluster Proximity
    • 5.3.4 Key Issues in Hierarchical Clustering
    • 5.3.5 Outliers
    • 5.3.6 Strengths and Weaknesses
  • 5.4 DBSCAN
    • 5.4.1 Traditional Density: Center-based Approach
    • 5.4.2 The Dbscan Algorithm
    • 5.4.3 Strengths and Weaknesses
  • 5.5 Cluster Evaluation
    • 5.5.1 Overview
    • 5.5.2 Unsupervised Cluster Evaluation Using Cohesion and Separation
    • 5.5.3 Unsupervised Cluster Evaluation Using the Proximity Matrix
    • 5.5.4 Unsupervised Evaluation of Hierarchical Clustering
    • 5.5.5 Determining the Correct Number of Clusters
    • 5.5.6 Clustering Tendency
    • 5.5.7 Supervised Measures of Cluster Validity
    • 5.5.8 Assessing the Significance of Cluster Validity Measures
    • 5.5.9 Choosing a Cluster Validity Measure
  • 5.6 Bibliographic Notes
  • 5.7 Exercises
• 6 Classification: Alternative Techniques
  • 6.1 Types of Classifiers
  • 6.2 Rule-Based Classifier
    • 6.2.1 How a Rule-Based Classifier Works
    • 6.2.2 Properties of a Rule Set
    • 6.2.3 Direct Methods for Rule Extraction
    • 6.2.4 Indirect Methods for Rule Extraction
    • 6.2.5 Characteristics of Rule-Based Classifiers
  • 6.3 Nearest Neighbor Classifiers
    • 6.3.1 Algorithm
    • 6.3.2 Characteristics of Nearest Neighbor Classifiers
  • 6.4 Na¨ive Bayes Classifier
    • 6.4.1 Basics of Probability Theory
    • 6.4.2 Na¨ive Bayes Assumption
  • 6.5 Bayesian Networks
    • 6.5.1 Graphical Representation
    • 6.5.2 Inference and Learning
    • 6.5.3 Characteristics of Bayesian Networks
  • 6.6 Logistic Regression
    • 6.6.1 Logistic Regression as a Generalized Linear Model
    • 6.6.2 Learning Model Parameters
    • 6.6.3 Characteristics of Logistic Regression
  • 6.7 Artificial Neural Network (ann)
    • 6.7.1 Perceptron
    • 6.7.2 Multi-layer Neural Network
    • 6.7.3 Characteristics of Ann
  • 6.8 Deep Learning
    • 6.8.1 Using Synergistic Loss Functions
    • 6.8.2 Using Responsive Activation Functions
    • 6.8.3 Regularization
    • 6.8.4 Initialization of Model Parameters
    • 6.8.5 Characteristics of Deep Learning
  • 6.9 Support Vector Machine (svm)
    • 6.9.1 Margin of a Separating Hyperplane
    • 6.9.2 Linear SVM
    • 6.9.3 Soft-margin SVM
    • 6.9.4 Nonlinear SVM
    • 6.9.5 Characteristics of SVM
  • 6.10 Ensemble Methods
    • 6.10.1 Rationale for Ensemble Method
    • 6.10.2 Methods for Constructing an Ensemble Classifier
    • 6.10.3 Bias-Variance Decomposition
    • 6.10.4 Bagging
    • 6.10.5 Boosting
    • 6.10.6 Random Forests
    • 6.10.7 Empirical Comparison Among Ensemble Methods
  • 6.11 Class Imbalance Problem
    • 6.11.1 Building Classifiers with Class Imbalance
    • 6.11.2 Evaluating Performance with Class Imbalance
    • 6.11.3 Finding an Optimal Score Threshold
    • 6.11.4 Aggregate Evaluation of Performance
  • 6.12 Multiclass Problem
  • 6.13 Bibliographic Notes
  • 6.14 Exercises
• 7 Association Analysis: Advanced Concepts
  • 7.1 Handling Categorical Attributes
  • 7.2 Handling Continuous Attributes
    • 7.2.1 Discretization-Based Methods
    • 7.2.2 Statistics-Based Methods
    • 7.2.3 Non-Discretization Methods
  • 7.3 Handling a Concept Hierarchy
  • 7.4 Sequential Patterns
    • 7.4.1 Preliminaries
    • 7.4.2 Sequential Pattern Discovery
    • 7.4.3 Timing Constraints*
    • 7.4.4 Alternative Counting Schemes*
  • 7.5 Subgraph Patterns
    • 7.5.1 Preliminaries
    • 7.5.2 Frequent Subgraph Mining
    • 7.5.3 Candidate Generation
    • 7.5.4 Candidate Pruning
    • 7.5.5 Support Counting
  • 7.6 Infrequent Patterns*
    • 7.6.1 Negative Patterns
    • 7.6.2 Negatively Correlated Patterns
    • 7.6.3 Comparisons Among Infrequent Patterns, Negative Patterns, and Negatively Correlated Patterns
    • 7.6.4 Techniques for Mining Interesting Infrequent Patterns
    • 7.6.5 Techniques Based on Mining Negative Patterns
    • 7.6.6 Techniques Based on Support Expectation
  • 7.7 Bibliographic Notes
  • 7.8 Exercises
• 8 Cluster Analysis: Additional Issues and Algorithms
  • 8.1 Characteristics of Data, Clusters, and Clustering Algorithms
    • 8.1.1 Example: Comparing K-means and Dbscan
    • 8.1.2 Data Characteristics
    • 8.1.3 Cluster Characteristics
    • 8.1.4 General Characteristics of Clustering Algorithms
    • Road Map
  • 8.2 Prototype-based Clustering
    • 8.2.1 Fuzzy Clustering
    • 8.2.2 Clustering Using Mixture Models
    • 8.2.3 Self-organizing Maps (SOM)
  • 8.3 Density-Based Clustering
    • 8.3.1 Grid-Based Clustering
    • 8.3.2 Subspace Clustering
    • 8.3.3 Denclue: A Kernel-Based Scheme for Density-based Clustering
  • 8.4 Graph-Based Clustering
    • 8.4.1 Sparsification
    • 8.4.2 Minimum Spanning Tree (MST) Clustering
    • 8.4.3 Opossum: Optimal Partitioning of Sparse Similarities Using Metis
    • 8.4.4 Chameleon: Hierarchical Clustering with Dynamic Modeling
    • 8.4.5 Spectral Clustering
    • 8.4.6 Shared Nearest Neighbor Similarity
    • 8.4.7 the Jarvis-patrick Clustering Algorithm
    • 8.4.8 SNN Density
    • 8.4.9 SNN Density-Based Clustering
  • 8.5 Scalable Clustering Algorithms
    • 8.5.1 Scalability: General Issues and Approaches
    • 8.5.2 Birch
    • 8.5.3 Cure
  • 8.6 Which Clustering Algorithm?
  • 8.7 Bibliographic Notes
  • 8.8 Exercises
• 9 Anomaly Detection
  • 9.1 Characteristics of Anomaly Detection Problems
    • 9.1.1 A Definition of an Anomaly
    • 9.1.2 Nature of Data
    • 9.1.3 How Anomaly Detection is Used
  • 9.2 Characteristics of Anomaly Detection Methods
  • 9.3 Statistical Approaches
    • 9.3.1 Using Parametric Models
    • 9.3.2 Using Non-Parametric Models
    • 9.3.3 Modeling Normal and Anomalous Classes
    • 9.3.4 Assessing Statistical Significance
    • 9.3.5 Strengths and Weaknesses
  • 9.4 Proximity-Based Approaches
    • 9.4.1 Distance-Based Anomaly Score
    • 9.4.2 Density-Based Anomaly Score
    • 9.4.3 Relative Density-Based Anomaly Score
    • 9.4.4 Strengths and Weaknesses
  • 9.5 Clustering-Based Approaches
    • 9.5.1 Finding Anomalous Clusters
    • 9.5.2 Finding Anomalous Instances
    • 9.5.3 Strengths and Weaknesses
  • 9.6 Reconstruction-Based Approaches
    • 9.6.1 Strengths and Weaknesses
  • 9.7 One-Class Classification
    • 9.7.1 Use of Kernels
    • 9.7.2 The Origin Trick
    • 9.7.3 Strengths and Weaknesses
  • 9.8 Information Theoretic Approaches
    • 9.8.1 Strengths and Weaknesses
  • 9.9 Evaluation of Anomaly Detection
  • 9.10 Bibliographic Notes
  • 9.11 Exercises
• 10 Avoiding False Discoveries
  • 10.1 Preliminaries: Statistical Testing
    • 10.1.1 Significance Testing
    • 10.1.2 Hypothesis Testing
    • 10.1.3 Multiple Hypothesis Testing
    • 10.1.4 Pitfalls in Statistical Testing
  • 10.2 Modeling Null and Alternative Distributions
    • 10.2.1 Generating Synthetic Data Sets
    • 10.2.2 Randomizing Class Labels
    • 10.2.3 Resampling Instances
    • 10.2.4 Modeling the Distribution of the Test Statistic
  • 10.3 Statistical Testing for Classification
    • 10.3.1 Evaluating Classification Performance
    • 10.3.2 Binary Classification as Multiple Hypothesis Testing
    • 10.3.3 Multiple Hypothesis Testing in Model Selection
  • 10.4 Statistical Testing for Association Analysis
    • 10.4.1 Using Statistical Models
    • 10.4.2 Using Randomization Methods
  • 10.5 Statistical Testing for Cluster Analysis
    • 10.5.1 Generating a Null Distribution for Internal Indices
    • 10.5.2 Generating a Null Distribution for External Indices
    • 10.5.3 Enrichment
  • 10.6 Statistical Testing for Anomaly Detection
  • 10.7 Bibliographic Notes
  • 10.8 Exercises
• Author Index
• Subject Index
• Copyright Permissions
• Back Cover

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