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• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgments
• Author
• Part I An Overview of Data Mining
• 1. Introduction to Data, Data Patterns, and Data Mining
• 1.1 Examples of Small Data Sets
• 1.2 Types of Data Variables
• 1.2.1 Attribute Variable versus Target Variable
• 1.2.2 Categorical Variable versus Numeric Variable
• 1.3 Data Patterns Learned through Data Mining
• 1.3.1 Classification and Prediction Patterns
• 1.3.2 Cluster and Association Patterns
• 1.3.3 Data Reduction Patterns
• 1.3.4 Outlier and Anomaly Patterns
• 1.3.5 Sequential and Temporal Patterns
• 1.4 Training Data and Test Data
• Exercises
• Part II Algorithms for Mining Classification and Prediction Patterns
• 2. Linear and Nonlinear Regression Models
• 2.1 Linear Regression Models
• 2.2 Least-Squares Method and Maximum Likelihood Method of Parameter Estimation
• 2.3 Nonlinear Regression Models and Parameter Estimation
• 2.4 Software and Applications
• Exercises
• 3. Naïve Bayes Classifier
• 3.1 Bayes Theorem
• 3.2 Classification Based on the Bayes Theorem and Naïve Bayes Classifier
• 3.3 Software and Applications
• Exercises
• 4. Decision and Regression Trees
• 4.1 Learning a Binary Decision Tree and Classifying Data Using a Decision Tree
• 4.1.1 Elements of a Decision Tree
• 4.1.2 Decision Tree with the Minimum Description Length
• 4.1.3 Split Selection Methods
• 4.1.4 Algorithm for the Top-Down Construction of a Decision Tree
• 4.1.5 Classifying Data Using a Decision Tree
• 4.2 Learning a Nonbinary Decision Tree
• 4.3 Handling Numeric and Missing Values of Attribute Variables
• 4.4 Handling a Numeric Target Variable and Constructing a Regression Tree
• 4.5 Advantages and Shortcomings of the Decision Tree Algorithm
• 4.6 Software and Applications
• Exercises
• 5. Artificial Neural Networks for Classification and Prediction
• 5.1 Processing Units of ANNs
• 5.2 Architectures of ANNs
• 5.3 Methods of Determining Connection Weights for a Perceptron
• 5.3.1 Perceptron
• 5.3.2 Properties of a Processing Unit
• 5.3.3 Graphical Method of Determining Connection Weights and Biases
• 5.3.4 Learning Method of Determining Connection Weights and Biases
• 5.3.5 Limitation of a Perceptron
• 5.4 Back-Propagation Learning Method for a Multilayer Feedforward ANN
• 5.5 Empirical Selection of an ANN Architecture for a Good Fit to Data
• 5.6 Software and Applications
• Exercises
• 6. Support Vector Machines
• 6.1 Theoretical Foundation for Formulating and Solving an Optimization Problem to Learn a Classification Function
• 6.2 SVM Formulation for a Linear Classifier and a Linearly Separable Problem
• 6.3 Geometric Interpretation of the SVM Formulation for the Linear Classifier
• 6.4 Solution of the Quadratic Programming Problem for a Linear Classifier
• 6.5 SVM Formulation for a Linear Classifier and a Nonlinearly Separable Problem
• 6.6 SVM Formulation for a Nonlinear Classifier and a Nonlinearly Separable Problem
• 6.7 Methods of Using SVM for Multi-Class Classification Problems
• 6.8 Comparison of ANN and SVM
• 6.9 Software and Applications
• Exercises
• 7. k-Nearest Neighbor Classifier and Supervised Clustering
• 7.1 k-Nearest Neighbor Classifier
• 7.2 Supervised Clustering
• 7.3 Software and Applications
• Exercises
• Part III Algorithms for Mining Cluster and Association Patterns
• 8. Hierarchical Clustering
• 8.1 Procedure of Agglomerative Hierarchical Clustering
• 8.2 Methods of Determining the Distance between Two Clusters
• 8.3 Illustration of the Hierarchical Clustering Procedure
• 8.4 Nonmonotonic Tree of Hierarchical Clustering
• 8.5 Software and Applications
• Exercises
• 9. K-Means Clustering and Density-Based Clustering
• 9.1 K-Means Clustering
• 9.2 Density-Based Clustering
• 9.3 Software and Applications
• Exercises
• 10. Self-Organizing Map
• 10.1 Algorithm of Self-Organizing Map
• 10.2 Software and Applications
• Exercises
• 11. Probability Distributions of Univariate Data
• 11.1 Probability Distribution of Univariate Data and Probability Distribution Characteristics of Various Data Patterns
• 11.2 Method of Distinguishing Four Probability Distributions
• 11.3 Software and Applications
• Exercises
• 12. Association Rules
• 12.1 Definition of Association Rules and Measures of Association
• 12.2 Association Rule Discovery
• 12.3 Software and Applications
• Exercises
• 13. Bayesian Network
• 13.1 Structure of a Bayesian Network and Probability Distributions of Variables
• 13.2 Probabilistic Inference
• 13.3 Learning of a Bayesian Network
• 13.4 Software and Applications
• Exercises
• Part IV Algorithms for Mining Data Reduction Patterns
• 14. Principal Component Analysis
• 14.1 Review of Multivariate Statistics
• 14.2 Review of Matrix Algebra
• 14.3 Principal Component Analysis
• 14.4 Software and Applications
• Exercises
• 15. Multidimensional Scaling
• 15.1 Algorithm of MDS
• 15.2 Number of Dimensions
• 15.3 INDSCALE for Weighted MDS
• 15.4 Software and Applications
• Exercises
• Part V Algorithms for Mining Outlier and Anomaly Patterns
• 16. Univariate Control Charts
• 16.1 Shewhart Control Charts
• 16.2 CUSUM Control Charts
• 16.3 EWMA Control Charts
• 16.4 Cuscore Control Charts
• 16.5 Receiver Operating Curve (ROC) for Evaluation and Comparison of Control Charts
• 16.6 Software and Applications
• Exercises
• 17. Multivariate Control Charts
• 17.1 Hotelling’s T2 Control Charts
• 17.2 Multivariate EWMA Control Charts
• 17.3 Chi-Square Control Charts
• 17.4 Applications
• Exercises
• Part VI Algorithms for Mining Sequential and Temporal Patterns
• 18. Autocorrelation and Time Series Analysis
• 18.1 Autocorrelation
• 18.2 Stationarity and Nonstationarity
• 18.3 ARMA Models of Stationary Series Data
• 18.4 ACF and PACF Characteristics of ARMA Models
• 18.5 Transformations of Nonstationary Series Data and ARIMA Models
• 18.6 Software and Applications
• Exercises
• 19. Markov Chain Models and Hidden Markov Models
• 19.1 Markov Chain Models
• 19.2 Hidden Markov Models
• 19.3 Learning Hidden Markov Models
• 19.4 Software and Applications
• Exercises
• 20. Wavelet Analysis
• 20.1 Definition of Wavelet
• 20.2 Wavelet Transform of Time Series Data
• 20.3 Reconstruction of Time Series Data from Wavelet Coefficients
• 20.4 Software and Applications
• Exercises
• References
• Index