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Contents

Title and Copyright Information

Preface

• Audience
• Documentation Accessibility
• Related Documentation
  • Oracle Data Mining Resources on the Oracle Technology Network
  • Application Development and Database Administration Documentation
• Conventions

Part I Introductions

1 What Is Data Mining?

• What Is Data Mining?
  • Automatic Discovery
  • Prediction
  • Grouping
  • Actionable Information
  • Data Mining and Statistics
  • Data Mining and OLAP
  • Data Mining and Data Warehousing
• What Can Data Mining Do and Not Do?
  • Asking the Right Questions
  • Understanding Your Data
• The Data Mining Process
  • Problem Definition
  • Data Gathering, Preparation, and Feature Engineering
  • Model Building and Evaluation
  • Knowledge Deployment

2 Introduction to Oracle Data Mining

• About Oracle Data Mining
• Data Mining in the Database Kernel
• Oracle Data Mining with R Extensibility
• Data Mining in Oracle Exadata
• About Partitioned Model
• Interfaces to Oracle Data Mining
  • PL/SQL API
  • SQL Functions
  • Oracle Data Miner
  • Predictive Analytics
• Overview of Database Analytics

3 Oracle Data Mining Basics

• Mining Functions
  • Supervised Data Mining
    • Supervised Learning: Testing
    • Supervised Learning: Scoring
  • Unsupervised Data Mining
    • Unsupervised Learning: Scoring
• Algorithms
  • Oracle Data Mining Supervised Algorithms
  • Oracle Data Mining Unsupervised Algorithms
• Data Preparation
  • Oracle Data Mining Simplifies Data Preparation
  • Case Data
    • Nested Data
  • Text Data
• In-Database Scoring
  • Parallel Execution and Ease of Administration
  • SQL Functions for Model Apply and Dynamic Scoring

Part II Mining Functions

4 Regression

• About Regression
  • How Does Regression Work?
    • Linear Regression
    • Multivariate Linear Regression
    • Regression Coefficients
    • Nonlinear Regression
    • Multivariate Nonlinear Regression
    • Confidence Bounds
• Testing a Regression Model
  • Regression Statistics
    • Root Mean Squared Error
    • Mean Absolute Error
• Regression Algorithms

5 Classification

• About Classification
• Testing a Classification Model
  • Confusion Matrix
  • Lift
    • Lift Statistics
  • Receiver Operating Characteristic (ROC)
    • The ROC Curve
    • Area Under the Curve
    • ROC and Model Bias
    • ROC Statistics
• Biasing a Classification Model
  • Costs
    • Costs Versus Accuracy
    • Positive and Negative Classes
    • Assigning Costs and Benefits
  • Priors and Class Weights
• Classification Algorithms

6 Anomaly Detection

• About Anomaly Detection
  • One-Class Classification
  • Anomaly Detection for Single-Class Data
  • Anomaly Detection for Finding Outliers
• Anomaly Detection Algorithm

7 Clustering

• About Clustering
  • How are Clusters Computed?
  • Scoring New Data
  • Hierarchical Clustering
    • Rules
    • Support and Confidence
• Evaluating a Clustering Model
• Clustering Algorithms

8 Association

• About Association
  • Association Rules
  • Market-Basket Analysis
  • Association Rules and eCommerce
• Transactional Data
• Association Algorithm

9 Feature Selection and Extraction

• Finding the Best Attributes
• About Feature Selection and Attribute Importance
  • Attribute Importance and Scoring
• About Feature Extraction
  • Feature Extraction and Scoring
• Algorithms for Attribute Importance and Feature Extraction

Part III Algorithms

10 Apriori

• About Apriori
• Association Rules and Frequent Itemsets
  • Antecedent and Consequent
  • Confidence
• Data Preparation for Apriori
  • Native Transactional Data and Star Schemas
  • Items and Collections
  • Sparse Data
• Calculating Association Rules
  • Itemsets
  • Frequent Itemsets
  • Example: Calculating Rules from Frequent Itemsets
  • Aggregates
  • Example: Calculating Aggregates
  • Including and Excluding Rules
  • Performance Impact for Aggregates
• Evaluating Association Rules
  • Support
  • Minimum Support Count
  • Confidence
  • Reverse Confidence
  • Lift

11 Decision Tree

• About Decision Tree
  • Decision Tree Rules
    • Confidence and Support
  • Advantages of Decision Trees
  • XML for Decision Tree Models
• Growing a Decision Tree
  • Splitting
  • Cost Matrix
  • Preventing Over-Fitting
• Tuning the Decision Tree Algorithm
• Data Preparation for Decision Tree

12 Expectation Maximization

• About Expectation Maximization
  • Expectation Step and Maximization Step
  • Probability Density Estimation
• Algorithm Enhancements
  • Scalability
  • High Dimensionality
  • Number of Components
  • Parameter Initialization
  • From Components to Clusters
• Configuring the Algorithm
• Data Preparation for Expectation Maximization

13 Explicit Semantic Analysis

• About Explicit Semantic Analysis
  • Scoring with ESA
  • Scoring Large ESA Models
• ESA for Text Mining
• Data Preparation for ESA

14 Generalized Linear Models

• About Generalized Linear Models
• GLM in Oracle Data Mining
  • Interpretability and Transparency
  • Wide Data
  • Confidence Bounds
  • Ridge Regression
    • Configuring Ridge Regression
    • Ridge and Confidence Bounds
    • Ridge and Data Preparation
• Scalable Feature Selection
  • Feature Selection
    • Configuring Feature Selection
    • Feature Selection and Ridge Regression
  • Feature Generation
    • Configuring Feature Generation
• Tuning and Diagnostics for GLM
  • Build Settings
  • Diagnostics
    • Coefficient Statistics
    • Global Model Statistics
    • Row Diagnostics
• Data Preparation for GLM
  • Data Preparation for Linear Regression
  • Data Preparation for Logistic Regression
  • Missing Values
• Linear Regression
  • Coefficient Statistics for Linear Regression
  • Global Model Statistics for Linear Regression
  • Row Diagnostics for Linear Regression
• Logistic Regression
  • Reference Class
  • Class Weights
  • Coefficient Statistics for Logistic Regression
  • Global Model Statistics for Logistic Regression
  • Row Diagnostics for Logistic Regression

15 k-Means

• About k-Means
  • Oracle Data Mining Enhanced k-Means
  • Centroid
• k-Means Algorithm Configuration
• Data Preparation for k-Means

16 Minimum Description Length

• About MDL
  • Compression and Entropy
    • Values of a Random Variable: Statistical Distribution
    • Values of a Random Variable: Significant Predictors
    • Total Entropy
  • Model Size
  • Model Selection
  • The MDL Metric
• Data Preparation for MDL

17 Naive Bayes

• About Naive Bayes
  • Advantages of Naive Bayes
• Tuning a Naive Bayes Model
• Data Preparation for Naive Bayes

18 Non-Negative Matrix Factorization

• About NMF
  • Matrix Factorization
  • Scoring with NMF
  • Text Mining with NMF
• Tuning the NMF Algorithm
• Data Preparation for NMF

19 O-Cluster

• About O-Cluster
  • Partitioning Strategy
    • Partitioning Numerical Attributes
    • Partitioning Categorical Attributes
  • Active Sampling
  • Process Flow
  • Scoring
• Tuning the O-Cluster Algorithm
• Data Preparation for O-Cluster
  • User-Specified Data Preparation for O-Cluster

20 Singular Value Decomposition

• About Singular Value Decomposition
  • Matrix Manipulation
  • Low Rank Decomposition
  • Scalability
• Configuring the Algorithm
  • Model Size
  • Performance
  • PCA scoring
• Data Preparation for SVD

21 Support Vector Machines

• About Support Vector Machines
  • Advantages of SVM
  • Advantages of SVM in Oracle Data Mining
    • Usability
    • Scalability
  • Kernel-Based Learning
• Tuning an SVM Model
• Data Preparation for SVM
  • Normalization
  • SVM and Automatic Data Preparation
• SVM Classification
  • Class Weights
• One-Class SVM
• SVM Regression

Glossary

Index