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