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Contents

List of Tables

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

Changes in This Release for Oracle Data Mining User's Guide

• Oracle Data Mining User's Guide is New in This Release
• Changes in Oracle Data Mining 12c Release 2 (12.2)
  • New Features in 12c Release 2
• Changes in Oracle Data Mining 12c Release 1 (12.1)
  • New Features
  • Desupported Features
  • Other Changes

1 Data Mining With SQL

• Highlights of the Data Mining API
• Example: Targeting Likely Candidates for a Sales Promotion
• Example: Analyzing Preferred Customers
• Example: Segmenting Customer Data
• Example : Building an ESA Model with a Wiki Dataset

2 About the Data Mining API

• About Mining Models
• Data Mining Data Dictionary Views
  • ALL_MINING_MODELS
  • ALL_MINING_MODEL_ATTRIBUTES
  • ALL_MINING_MODEL_PARTITIONS
  • ALL_MINING_MODEL_SETTINGS
  • ALL_MINING_MODEL_VIEWS
  • ALL_MINING_MODEL_XFORMS
• Data Mining PL/SQL Packages
  • DBMS_DATA_MINING
  • DBMS_DATA_MINING_TRANSFORM
    • Transformation Methods in DBMS_DATA_MINING_TRANSFORM
  • DBMS_PREDICTIVE_ANALYTICS
• Data Mining SQL Scoring Functions

3 Preparing the Data

• Data Requirements
  • Column Data Types
  • Data Sets for Classification and Regression
  • Scoring Requirements
• About Attributes
  • Data Attributes and Model Attributes
  • Target Attribute
  • Numericals, Categoricals, and Unstructured Text
  • Model Signature
  • Scoping of Model Attribute Name
  • Model Details
• Using Nested Data
  • Nested Object Types
  • Example: Transforming Transactional Data for Mining
• Using Market Basket Data
  • Example: Creating a Nested Column for Market Basket Analysis
• Using Retail Analysis Data
  • Example: Calculating Aggregates
• Handling Missing Values
  • Examples: Missing Values or Sparse Data?
    • Sparsity in a Sales Table
    • Missing Values in a Table of Customer Data
  • Missing Value Treatment in Oracle Data Mining
  • Changing the Missing Value Treatment

4 Transforming the Data

• About Transformations
• Preparing the Case Table
  • Creating Nested Columns
  • Converting Column Data Types
  • Text Transformation
  • About Business and Domain-Sensitive Transformations
• Understanding Automatic Data Preparation
  • Binning
  • Normalization
  • Outlier Treatment
  • How ADP Transforms the Data
• Embedding Transformations in a Model
  • Specifying Transformation Instructions for an Attribute
    • Expression Records
    • Attribute Specifications
  • Building a Transformation List
    • SET_TRANSFORM
    • The STACK Interface
    • GET_MODEL_TRANSFORMATIONS and GET_TRANSFORM_LIST
  • Transformation Lists and Automatic Data Preparation
  • Oracle Data Mining Transformation Routines
    • Binning Routines
    • Normalization Routines
    • Routines for Outlier Treatment
• Understanding Reverse Transformations

5 Creating a Model

• Before Creating a Model
• The CREATE_MODEL Procedure
  • Choosing the Mining Function
  • Choosing the Algorithm
  • Supplying Transformations
    • Creating a Transformation List
    • Transformation List and Automatic Data Preparation
  • About Partitioned Model
    • Partitioned Model Build Process
    • DDL in Partitioned model
      • Drop Model or Drop Partition
      • Add Partition
    • Partitioned Model scoring
• Specifying Model Settings
  • Specifying Costs
  • Specifying Prior Probabilities
  • Specifying Class Weights
  • Model Settings in the Data Dictionary
  • Specifying Mining Model Settings for R Model
    • ALGO_EXTENSIBLE_LANG
    • RALG_BUILD_FUNCTION
      • RALG_BUILD_PARAMETER
    • RALG_DETAILS_FUNCTION
      • RALG_DETAILS_FORMAT
    • RALG_SCORE_FUNCTION
    • RALG_WEIGHT_FUNCTION
    • Registered R Scripts
    • R Model Demonstration Scripts
• Model Detail Views
  • Model Detail Views for Association Rules
  • Model Detail View for Frequent Itemsets
  • Model Detail View for Transactional Itemsets
  • Model Detail View for Transactional Rule
  • Model Detail Views for Classification Algorithms
  • Model Detail Views for Decision Tree
  • Model Detail Views for Generalized Linear Model
  • Model Detail Views for Naive Bayes
  • Model Detail View for Support Vector Machine
  • Model Detail Views for Clustering Algorithms
  • Model Detail Views for Expectation Maximization
  • Model Detail Views for k-Means
  • Model Detail Views for O-Cluster
  • Model Detail Views for Explicit Semantic Analysis
  • Model Detail Views for Non-Negative Matrix Factorization
  • Model Detail Views for Singular Value Decomposition
  • Model Detail View for Minimum Description Length
  • Model Detail View for Binning
  • Model Detail Views for Global Information
  • Model Detail View for Normalization and Missing Value Handling

6 Scoring and Deployment

• About Scoring and Deployment
• Using the Data Mining SQL Functions
  • Choosing the Predictors
  • Single-Record Scoring
• Prediction Details
  • Cluster Details
  • Feature Details
  • Prediction Details
  • GROUPING Hint
• Real-Time Scoring
• Dynamic Scoring
• Cost-Sensitive Decision Making
• DBMS_DATA_MINING.Apply

7 Mining Unstructured Text

• About Unstructured Text
• About Text Mining and Oracle Text
• Data Preparation for Text Features
• Creating a Model that Includes Text Mining
• Creating a Text Policy
• Configuring a Text Attribute

8 Administrative Tasks for Oracle Data Mining

• Installing and Configuring a Database for Data Mining
  • About Installation
  • Enabling or Disabling a Database Option
  • Database Tuning Considerations for Data Mining
• Upgrading or Downgrading Oracle Data Mining
  • Pre-Upgrade Steps
    • Dropping Models Created in Java
    • Dropping Mining Activities Created in Oracle Data Miner Classic
  • Upgrading Oracle Data Mining
    • Using Database Upgrade Assistant to Upgrade Oracle Data Mining
      • Upgrading from Release 10g
      • Upgrading from Release 11g
    • Using Export/Import to Upgrade Data Mining Models
      • Export/Import Release 10g Data Mining Models
      • Export/Import Release 11g Data Mining Models
  • Post Upgrade Steps
  • Downgrading Oracle Data Mining
• Exporting and Importing Mining Models
  • About Oracle Data Pump
  • Options for Exporting and Importing Mining Models
  • Directory Objects for EXPORT_MODEL and IMPORT_MODEL
  • Using EXPORT_MODEL and IMPORT_MODEL
  • Importing From PMML
• Controlling Access to Mining Models and Data
  • Creating a Data Mining User
    • Granting Privileges for Data Mining
  • System Privileges for Data Mining
  • Object Privileges for Mining Models
• Auditing and Adding Comments to Mining Models
  • Adding a Comment to a Mining Model
  • Auditing Mining Models

A The Data Mining Sample Programs

• About the Data Mining Sample Programs
• Installing the Data Mining Sample Programs
• The Data Mining Sample Data

Index