Contents

List of Examples

List of Figures

List of Tables

Title and Copyright Information

Preface

• Audience
• Documentation Accessibility
• Related Documentation
• Conventions

What's New in Oracle Data Mining?

• Oracle Data Mining 11g Release 1 (11.1) New Features
• Oracle Data Mining 10g Release 2 (10.2) New Features

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 and Preparation
  • Model Building and Evaluation
  • Knowledge Deployment

2 Introducing Oracle Data Mining

• Data Mining in the Database Kernel
• Data Mining Functions
  • Supervised Data Mining
    • Build Data and Test Data for Supervised Learning
    • Apply Data for Supervised Learning
  • Unsupervised Data Mining
    • Build Data for Unsupervised Learning
    • Apply Data for Unsupervised Learning
  • Oracle Data Mining Functions
• Data Mining Algorithms
  • Oracle Data Mining Supervised Algorithms
  • Oracle Data Mining Unsupervised Algorithms
• Data Preparation
  • Supermodels
• How Do I Use Oracle Data Mining?
  • PL/SQL Packages
  • SQL Functions
  • Java API
  • Oracle Spreadsheet Add-In for Predictive Analytics
• Where Do I Find Information About Oracle Data Mining?
  • Oracle Data Mining Resources on the Oracle Technology Network
  • Oracle Data Mining Publications
• Oracle Data Mining and Oracle Database Analytics

3 Introducing Oracle Predictive Analytics

• About Predictive Analytics
  • Predictive Analytics and Data Mining
  • How Does it Work?
  • Predictive Analytics Operations
• Oracle Spreadsheet Add-In for Predictive Analytics
• APIs for Predictive Analytics
  • Predictive Analytics in the PL/SQL API
  • Predictive Analytics in the Java API
    • Example: Use OraProfileTask to Create Profile Results
• Example: PREDICT
• Behind the Scenes
  • EXPLAIN
  • PREDICT
    • Accuracy
  • PROFILE

Part II Mining Functions

4 Regression

• About Regression
  • Common Applications of Regression
  • How Does Regression Work?
    • Linear Regression
    • Nonlinear Regression
    • Multivariate Regression
• Regression Algorithms
• Testing a Regression Model
  • Root Mean Squared Error
  • Mean Absolute Error

5 Classification

• About Classification
  • Binary and Multiclass Targets
  • Common Applications of Classification
• Classification Algorithms
• Biasing a Classification Model
  • Cost/Benefit Matrix
  • Priors
• Testing a Classification Model
  • Confusion Matrix
  • Lift
  • Receiver Operating Characteristic (ROC)

6 Anomaly Detection

• About Anomaly Detection
  • Counter-examples
  • Outliers
• One-Class Classification
• Anomaly Detection Algorithm

7 Clustering

• About Clustering
• Clustering Algorithms

8 Association Rules

• About Association Rules
  • Difficult Cases for Associations
    • Finding Associations Involving Rare Events
• Association Algorithm
• Data for Association Models

9 Feature Selection and Extraction

• Feature Extraction
  • Feature Extraction Algorithm
• Attribute Importance
  • Data Preparation for Attribute Importance
  • Attribute Importance Algorithm

Part III Algorithms

10 Apriori

• Association Rules and Frequent Item Sets
• Data Preparation for Association Rules

11 Decision Tree

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

12 Generalized Linear Models

• About Generalized Linear Models
  • GLM in Oracle Data Mining
    • Interpretability and Transparency
    • Wide Data
    • Confidence Bounds
  • Ridge Regression
    • Build Settings for Ridge Regression
    • Ridge and Confidence Bounds
    • Ridge and Variance Inflation Factor for Linear Regression
    • Ridge and Data Preparation
• 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
  • Coefficient Statistics for Logistic Regression
  • Global Model Statistics for Logistic Regression
  • Row Diagnostics for Logistic Regression

13 k-Means

• About k-Means
  • Scoring k-Means Clustering Models
• Data Preparation for k-Means

14 Minimum Description Length

• About MDL
• Data Preparation for MDL

15 Naive Bayes

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

16 Non-Negative Matrix Factorization

• About NMF
• NMF for Text Mining
• Data Preparation for NMF

17 O-Cluster

• About O-Cluster
• O-Cluster Scoring
• Data Preparation for O-Cluster

18 Support Vector Machines

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

Part IV Data Preparation

19 Automatic and Embedded Data Preparation

• Overview
  • The Case Table
  • Data Type Conversion
    • Date Data
  • Text Transformation
  • Business and Domain-Sensitive Transformations
• Automatic Data Preparation
  • Enabling Automatic Data Preparation
  • Overview of Algorithm-Specific Transformations
    • Binning
    • Normalization
    • Outlier Treatment
  • Algorithms and ADP
• Embedded Data Preparation
  • Transformation Lists and ADP
  • Creating a Transformation List
    • Transforming a Nested Attribute
  • Oracle Data Mining Transformation Routines
    • Binning
    • Normalization
    • Outlier Treatment
• Transparency
  • Model Details and the Build Data
  • Reverse Transformations
    • Altering the Reverse Transformation Expression

Part V Mining Unstructured Data

20 Text Mining

• Oracle Data Mining and Oracle Text
• What is Text Mining?
  • Document Classification
  • Combining Text and Structured Data
• Oracle Data Mining Support for Text Mining
  • Classification and Text Mining
  • Clustering and Text Mining
  • Feature Extraction and Text Mining
  • Association and Text Mining
  • Regression and Text Mining
  • Anomaly Detection and Text Mining
• Oracle Support for Text Mining

Glossary

Index