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  1. Database
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  3. Oracle Database
  4. Release 19

User's Guide

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Table of Contents

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  • 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 18c
      • New Features in 18c
      • Deprecated Features
      • Desupported Features
      • Other Changes
  • 1 Data Mining With SQL
    • 1.1 Highlights of the Data Mining API
    • 1.2 Example: Targeting Likely Candidates for a Sales Promotion
    • 1.3 Example: Analyzing Preferred Customers
    • 1.4 Example: Segmenting Customer Data
    • 1.5 Example : Building an ESA Model with a Wiki Dataset
  • 2 About the Data Mining API
    • 2.1 About Mining Models
    • 2.2 Data Mining Data Dictionary Views
      • 2.2.1 ALL_MINING_MODELS
      • 2.2.2 ALL_MINING_MODEL_ATTRIBUTES
      • 2.2.3 ALL_MINING_MODEL_PARTITIONS
      • 2.2.4 ALL_MINING_MODEL_SETTINGS
      • 2.2.5 ALL_MINING_MODEL_VIEWS
      • 2.2.6 ALL_MINING_MODEL_XFORMS
    • 2.3 Data Mining PL/SQL Packages
      • 2.3.1 DBMS_DATA_MINING
      • 2.3.2 DBMS_DATA_MINING_TRANSFORM
        • 2.3.2.1 Transformation Methods in DBMS_DATA_MINING_TRANSFORM
      • 2.3.3 DBMS_PREDICTIVE_ANALYTICS
    • 2.4 Data Mining SQL Scoring Functions
  • 3 Preparing the Data
    • 3.1 Data Requirements
      • 3.1.1 Column Data Types
      • 3.1.2 Data Sets for Classification and Regression
      • 3.1.3 Scoring Requirements
    • 3.2 About Attributes
      • 3.2.1 Data Attributes and Model Attributes
      • 3.2.2 Target Attribute
      • 3.2.3 Numericals, Categoricals, and Unstructured Text
      • 3.2.4 Model Signature
      • 3.2.5 Scoping of Model Attribute Name
      • 3.2.6 Model Details
    • 3.3 Using Nested Data
      • 3.3.1 Nested Object Types
      • 3.3.2 Example: Transforming Transactional Data for Mining
    • 3.4 Using Market Basket Data
      • 3.4.1 Example: Creating a Nested Column for Market Basket Analysis
    • 3.5 Using Retail Analysis Data
      • 3.5.1 Example: Calculating Aggregates
    • 3.6 Handling Missing Values
      • 3.6.1 Examples: Missing Values or Sparse Data?
        • 3.6.1.1 Sparsity in a Sales Table
        • 3.6.1.2 Missing Values in a Table of Customer Data
      • 3.6.2 Missing Value Treatment in Oracle Data Mining
      • 3.6.3 Changing the Missing Value Treatment
  • 4 Transforming the Data
    • 4.1 About Transformations
    • 4.2 Preparing the Case Table
      • 4.2.1 Creating Nested Columns
      • 4.2.2 Converting Column Data Types
      • 4.2.3 Text Transformation
      • 4.2.4 About Business and Domain-Sensitive Transformations
    • 4.3 Understanding Automatic Data Preparation
      • 4.3.1 Binning
      • 4.3.2 Normalization
      • 4.3.3 Outlier Treatment
      • 4.3.4 How ADP Transforms the Data
    • 4.4 Embedding Transformations in a Model
      • 4.4.1 Specifying Transformation Instructions for an Attribute
        • 4.4.1.1 Expression Records
        • 4.4.1.2 Attribute Specifications
      • 4.4.2 Building a Transformation List
        • 4.4.2.1 SET_TRANSFORM
        • 4.4.2.2 The STACK Interface
        • 4.4.2.3 GET_MODEL_TRANSFORMATIONS and GET_TRANSFORM_LIST
      • 4.4.3 Transformation Lists and Automatic Data Preparation
      • 4.4.4 Oracle Data Mining Transformation Routines
        • 4.4.4.1 Binning Routines
        • 4.4.4.2 Normalization Routines
        • 4.4.4.3 Routines for Outlier Treatment
    • 4.5 Understanding Reverse Transformations
  • 5 Creating a Model
    • 5.1 Before Creating a Model
    • 5.2 The CREATE_MODEL Procedure
      • 5.2.1 Choosing the Mining Function
      • 5.2.2 Choosing the Algorithm
      • 5.2.3 Supplying Transformations
        • 5.2.3.1 Creating a Transformation List
        • 5.2.3.2 Transformation List and Automatic Data Preparation
      • 5.2.4 About Partitioned Model
        • 5.2.4.1 Partitioned Model Build Process
        • 5.2.4.2 DDL in Partitioned model
          • 5.2.4.2.1 Drop Model or Drop Partition
          • 5.2.4.2.2 Add Partition
        • 5.2.4.3 Partitioned Model scoring
    • 5.3 Specifying Model Settings
      • 5.3.1 Specifying Costs
      • 5.3.2 Specifying Prior Probabilities
      • 5.3.3 Specifying Class Weights
      • 5.3.4 Model Settings in the Data Dictionary
      • 5.3.5 Specifying Mining Model Settings for R Model
        • 5.3.5.1 ALGO_EXTENSIBLE_LANG
        • 5.3.5.2 RALG_BUILD_FUNCTION
          • 5.3.5.2.1 RALG_BUILD_PARAMETER
        • 5.3.5.3 RALG_DETAILS_FUNCTION
          • 5.3.5.3.1 RALG_DETAILS_FORMAT
        • 5.3.5.4 RALG_SCORE_FUNCTION
        • 5.3.5.5 RALG_WEIGHT_FUNCTION
        • 5.3.5.6 Registered R Scripts
        • 5.3.5.7 R Model Demonstration Scripts
        • 5.3.5.8 Algorithm Meta Data Registration
    • 5.4 Model Detail Views
      • 5.4.1 Model Detail Views for Association Rules
      • 5.4.2 Model Detail View for Frequent Itemsets
      • 5.4.3 Model Detail View for Transactional Itemsets
      • 5.4.4 Model Detail View for Transactional Rule
      • 5.4.5 Model Detail Views for Classification Algorithms
      • 5.4.6 Model Detail Views for CUR Matrix Decomposition
      • 5.4.7 Model Detail Views for Decision Tree
      • 5.4.8 Model Detail Views for Generalized Linear Model
      • 5.4.9 Model Detail Views for Naive Bayes
      • 5.4.10 Model Detail Views for Neural Network
      • 5.4.11 Model Detail Views for Random Forest
      • 5.4.12 Model Detail View for Support Vector Machine
      • 5.4.13 Model Detail Views for Clustering Algorithms
      • 5.4.14 Model Detail Views for Expectation Maximization
      • 5.4.15 Model Detail Views for k-Means
      • 5.4.16 Model Detail Views for O-Cluster
      • 5.4.17 Model Detail Views for Explicit Semantic Analysis
      • 5.4.18 Model Detail Views for Non-Negative Matrix Factorization
      • 5.4.19 Model Detail Views for Singular Value Decomposition
      • 5.4.20 Model Detail View for Minimum Description Length
      • 5.4.21 Model Detail View for Binning
      • 5.4.22 Model Detail Views for Global Information
      • 5.4.23 Model Detail View for Normalization and Missing Value Handling
      • 5.4.24 Model Detail Views for Exponential Smoothing Models
  • 6 Scoring and Deployment
    • 6.1 About Scoring and Deployment
    • 6.2 Using the Data Mining SQL Functions
      • 6.2.1 Choosing the Predictors
      • 6.2.2 Single-Record Scoring
    • 6.3 Prediction Details
      • 6.3.1 Cluster Details
      • 6.3.2 Feature Details
      • 6.3.3 Prediction Details
      • 6.3.4 GROUPING Hint
    • 6.4 Real-Time Scoring
    • 6.5 Dynamic Scoring
    • 6.6 Cost-Sensitive Decision Making
    • 6.7 DBMS_DATA_MINING.Apply
  • 7 Mining Unstructured Text
    • 7.1 About Unstructured Text
    • 7.2 About Text Mining and Oracle Text
    • 7.3 Data Preparation for Text Features
    • 7.4 Creating a Model that Includes Text Mining
    • 7.5 Creating a Text Policy
    • 7.6 Configuring a Text Attribute
  • 8 Administrative Tasks for Oracle Data Mining
    • 8.1 Installing and Configuring a Database for Data Mining
      • 8.1.1 About Installation
      • 8.1.2 Enabling or Disabling a Database Option
      • 8.1.3 Database Tuning Considerations for Data Mining
    • 8.2 Upgrading or Downgrading Oracle Data Mining
      • 8.2.1 Pre-Upgrade Steps
        • 8.2.1.1 Dropping Models Created in Java
        • 8.2.1.2 Dropping Mining Activities Created in Oracle Data Miner Classic
      • 8.2.2 Upgrading Oracle Data Mining
        • 8.2.2.1 Using Database Upgrade Assistant to Upgrade Oracle Data Mining
          • 8.2.2.1.1 Upgrading from Release 10g
          • 8.2.2.1.2 Upgrading from Release 11g
        • 8.2.2.2 Using Export/Import to Upgrade Data Mining Models
          • 8.2.2.2.1 Export/Import Release 10g Data Mining Models
          • 8.2.2.2.2 Export/Import Release 11g Data Mining Models
      • 8.2.3 Post Upgrade Steps
      • 8.2.4 Downgrading Oracle Data Mining
    • 8.3 Exporting and Importing Mining Models
      • 8.3.1 About Oracle Data Pump
      • 8.3.2 Options for Exporting and Importing Mining Models
      • 8.3.3 Directory Objects for EXPORT_MODEL and IMPORT_MODEL
      • 8.3.4 Using EXPORT_MODEL and IMPORT_MODEL
      • 8.3.5 EXPORT and IMPORT Serialized Models
      • 8.3.6 Importing From PMML
    • 8.4 Controlling Access to Mining Models and Data
      • 8.4.1 Creating a Data Mining User
        • 8.4.1.1 Granting Privileges for Data Mining
      • 8.4.2 System Privileges for Data Mining
      • 8.4.3 Object Privileges for Mining Models
    • 8.5 Auditing and Adding Comments to Mining Models
      • 8.5.1 Adding a Comment to a Mining Model
      • 8.5.2 Auditing Mining Models
  • A The Data Mining Sample Programs
    • A.1 About the Data Mining Sample Programs
    • A.2 Installing the Data Mining Sample Programs
    • A.3 The Data Mining Sample Data
  • Index

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  1. User's Guide
  2. Data Mining With SQL

1 Data Mining With SQL

Learn how to solve business problems using the Oracle Data Mining application programming interface (API).

  • 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

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