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  4. Release 19

API Guide

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Table of Contents

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  • Title and Copyright Information
  • Preface
    • Audience
    • Documentation Accessibility
    • Conventions
  • Part I Introductions
    • 1 Introduction to Oracle Data Mining
      • 1.1 About Oracle Data Mining
      • 1.2 Data Mining in the Database Kernel
      • 1.3 Data Mining in Oracle Exadata
      • 1.4 About Partitioned Model
      • 1.5 Interfaces to Oracle Data Mining
        • 1.5.1 PL/SQL API
        • 1.5.2 SQL Functions
        • 1.5.3 Oracle Data Miner
        • 1.5.4 Predictive Analytics
      • 1.6 Overview of Database Analytics
    • 2 Oracle Data Mining Basics
      • 2.1 Mining Functions
        • 2.1.1 Supervised Data Mining
          • 2.1.1.1 Supervised Learning: Testing
          • 2.1.1.2 Supervised Learning: Scoring
        • 2.1.2 Unsupervised Data Mining
          • 2.1.2.1 Unsupervised Learning: Scoring
      • 2.2 Algorithms
        • 2.2.1 Oracle Data Mining Supervised Algorithms
        • 2.2.2 Oracle Data Mining Unsupervised Algorithms
      • 2.3 Data Preparation
        • 2.3.1 Oracle Data Mining Simplifies Data Preparation
        • 2.3.2 Case Data
          • 2.3.2.1 Nested Data
        • 2.3.3 Text Data
      • 2.4 In-Database Scoring
        • 2.4.1 Parallel Execution and Ease of Administration
        • 2.4.2 SQL Functions for Model Apply and Dynamic Scoring
  • Part II Mining Functions
    • 3 Regression
      • 3.1 About Regression
        • 3.1.1 How Does Regression Work?
          • 3.1.1.1 Linear Regression
          • 3.1.1.2 Multivariate Linear Regression
          • 3.1.1.3 Regression Coefficients
          • 3.1.1.4 Nonlinear Regression
          • 3.1.1.5 Multivariate Nonlinear Regression
          • 3.1.1.6 Confidence Bounds
      • 3.2 Testing a Regression Model
        • 3.2.1 Regression Statistics
          • 3.2.1.1 Root Mean Squared Error
          • 3.2.1.2 Mean Absolute Error
      • 3.3 Regression Algorithms
    • 4 Classification
      • 4.1 About Classification
      • 4.2 Testing a Classification Model
        • 4.2.1 Confusion Matrix
        • 4.2.2 Lift
          • 4.2.2.1 Lift Statistics
        • 4.2.3 Receiver Operating Characteristic (ROC)
          • 4.2.3.1 The ROC Curve
          • 4.2.3.2 Area Under the Curve
          • 4.2.3.3 ROC and Model Bias
          • 4.2.3.4 ROC Statistics
      • 4.3 Biasing a Classification Model
        • 4.3.1 Costs
          • 4.3.1.1 Costs Versus Accuracy
          • 4.3.1.2 Positive and Negative Classes
          • 4.3.1.3 Assigning Costs and Benefits
        • 4.3.2 Priors and Class Weights
      • 4.4 Classification Algorithms
    • 5 Anomaly Detection
      • 5.1 About Anomaly Detection
        • 5.1.1 One-Class Classification
        • 5.1.2 Anomaly Detection for Single-Class Data
        • 5.1.3 Anomaly Detection for Finding Outliers
      • 5.2 Anomaly Detection Algorithm
    • 6 Clustering
      • 6.1 About Clustering
        • 6.1.1 How are Clusters Computed?
        • 6.1.2 Scoring New Data
        • 6.1.3 Hierarchical Clustering
          • 6.1.3.1 Rules
          • 6.1.3.2 Support and Confidence
      • 6.2 Evaluating a Clustering Model
      • 6.3 Clustering Algorithms
    • 7 Association
      • 7.1 About Association
        • 7.1.1 Association Rules
        • 7.1.2 Market-Basket Analysis
        • 7.1.3 Association Rules and eCommerce
      • 7.2 Transactional Data
      • 7.3 Association Algorithm
    • 8 Feature Selection and Extraction
      • 8.1 Finding the Best Attributes
      • 8.2 About Feature Selection and Attribute Importance
        • 8.2.1 Attribute Importance and Scoring
      • 8.3 About Feature Extraction
        • 8.3.1 Feature Extraction and Scoring
      • 8.4 Algorithms for Attribute Importance and Feature Extraction
    • 9 Time Series
      • 9.1 About Time Series
      • 9.2 Choosing a Time Series Model
      • 9.3 Time Series Statistics
        • 9.3.1 Conditional Log-Likelihood
        • 9.3.2 Mean Square Error (MSE) and Other Error Measures
        • 9.3.3 Irregular Time Series
        • 9.3.4 Build Apply
      • 9.4 Time Series Algorithm
  • Part III Algorithms
    • 10 Apriori
      • 10.1 About Apriori
      • 10.2 Association Rules and Frequent Itemsets
        • 10.2.1 Antecedent and Consequent
        • 10.2.2 Confidence
      • 10.3 Data Preparation for Apriori
        • 10.3.1 Native Transactional Data and Star Schemas
        • 10.3.2 Items and Collections
        • 10.3.3 Sparse Data
        • 10.3.4 Improved Sampling
          • 10.3.4.1 Sampling Implementation
      • 10.4 Calculating Association Rules
        • 10.4.1 Itemsets
        • 10.4.2 Frequent Itemsets
        • 10.4.3 Example: Calculating Rules from Frequent Itemsets
        • 10.4.4 Aggregates
        • 10.4.5 Example: Calculating Aggregates
        • 10.4.6 Including and Excluding Rules
        • 10.4.7 Performance Impact for Aggregates
      • 10.5 Evaluating Association Rules
        • 10.5.1 Support
        • 10.5.2 Minimum Support Count
        • 10.5.3 Confidence
        • 10.5.4 Reverse Confidence
        • 10.5.5 Lift
    • 11 CUR Matrix Decomposition
      • 11.1 About CUR Matrix Decomposition
      • 11.2 Singular Vectors
      • 11.3 Statistical Leverage Score
      • 11.4 Column (Attribute) Selection and Row Selection
      • 11.5 CUR Matrix Decomposition Algorithm Configuration
    • 12 Decision Tree
      • 12.1 About Decision Tree
        • 12.1.1 Decision Tree Rules
          • 12.1.1.1 Confidence and Support
        • 12.1.2 Advantages of Decision Trees
        • 12.1.3 XML for Decision Tree Models
      • 12.2 Growing a Decision Tree
        • 12.2.1 Splitting
        • 12.2.2 Cost Matrix
        • 12.2.3 Preventing Over-Fitting
      • 12.3 Tuning the Decision Tree Algorithm
      • 12.4 Data Preparation for Decision Tree
    • 13 Expectation Maximization
      • 13.1 About Expectation Maximization
        • 13.1.1 Expectation Step and Maximization Step
        • 13.1.2 Probability Density Estimation
      • 13.2 Algorithm Enhancements
        • 13.2.1 Scalability
        • 13.2.2 High Dimensionality
        • 13.2.3 Number of Components
        • 13.2.4 Parameter Initialization
        • 13.2.5 From Components to Clusters
      • 13.3 Configuring the Algorithm
      • 13.4 Data Preparation for Expectation Maximization
    • 14 Explicit Semantic Analysis
      • 14.1 About Explicit Semantic Analysis
        • 14.1.1 Scoring with ESA
        • 14.1.2 Scoring Large ESA Models
      • 14.2 ESA for Text Mining
      • 14.3 Data Preparation for ESA
      • 14.4 Terminologies in Explicit Semantic Analysis
    • 15 Exponential Smoothing
      • 15.1 About Exponential Smoothing
        • 15.1.1 Exponential Smoothing Models
        • 15.1.2 Simple Exponential Smoothing
        • 15.1.3 Models with Trend but No Seasonality
        • 15.1.4 Models with Seasonality but No Trend
        • 15.1.5 Models with Trend and Seasonality
        • 15.1.6 Prediction Intervals
      • 15.2 Data Preparation for Exponential Smoothing Models
        • 15.2.1 Input Data
        • 15.2.2 Accumulation
        • 15.2.3 Missing Value
        • 15.2.4 Prediction
        • 15.2.5 Parallellism by Partition
    • 16 Generalized Linear Models
      • 16.1 About Generalized Linear Models
      • 16.2 GLM in Oracle Data Mining
        • 16.2.1 Interpretability and Transparency
        • 16.2.2 Wide Data
        • 16.2.3 Confidence Bounds
        • 16.2.4 Ridge Regression
          • 16.2.4.1 Configuring Ridge Regression
          • 16.2.4.2 Ridge and Confidence Bounds
          • 16.2.4.3 Ridge and Data Preparation
      • 16.3 Scalable Feature Selection
        • 16.3.1 Feature Selection
          • 16.3.1.1 Configuring Feature Selection
          • 16.3.1.2 Feature Selection and Ridge Regression
        • 16.3.2 Feature Generation
          • 16.3.2.1 Configuring Feature Generation
      • 16.4 Tuning and Diagnostics for GLM
        • 16.4.1 Build Settings
        • 16.4.2 Diagnostics
          • 16.4.2.1 Coefficient Statistics
          • 16.4.2.2 Global Model Statistics
          • 16.4.2.3 Row Diagnostics
      • 16.5 GLM Solvers
      • 16.6 Data Preparation for GLM
        • 16.6.1 Data Preparation for Linear Regression
        • 16.6.2 Data Preparation for Logistic Regression
        • 16.6.3 Missing Values
      • 16.7 Linear Regression
        • 16.7.1 Coefficient Statistics for Linear Regression
        • 16.7.2 Global Model Statistics for Linear Regression
        • 16.7.3 Row Diagnostics for Linear Regression
      • 16.8 Logistic Regression
        • 16.8.1 Reference Class
        • 16.8.2 Class Weights
        • 16.8.3 Coefficient Statistics for Logistic Regression
        • 16.8.4 Global Model Statistics for Logistic Regression
        • 16.8.5 Row Diagnostics for Logistic Regression
    • 17 k-Means
      • 17.1 About k-Means
        • 17.1.1 Oracle Data Mining Enhanced k-Means
        • 17.1.2 Centroid
      • 17.2 k-Means Algorithm Configuration
      • 17.3 Data Preparation for k-Means
    • 18 Minimum Description Length
      • 18.1 About MDL
        • 18.1.1 Compression and Entropy
          • 18.1.1.1 Values of a Random Variable: Statistical Distribution
          • 18.1.1.2 Values of a Random Variable: Significant Predictors
          • 18.1.1.3 Total Entropy
        • 18.1.2 Model Size
        • 18.1.3 Model Selection
        • 18.1.4 The MDL Metric
      • 18.2 Data Preparation for MDL
    • 19 Naive Bayes
      • 19.1 About Naive Bayes
        • 19.1.1 Advantages of Naive Bayes
      • 19.2 Tuning a Naive Bayes Model
      • 19.3 Data Preparation for Naive Bayes
    • 20 Neural Network
      • 20.1 About Neural Network
        • 20.1.1 Neuron and activation function
        • 20.1.2 Loss or Cost function
        • 20.1.3 Forward-Backward Propagation
        • 20.1.4 Optimization Solver
        • 20.1.5 Regularization
        • 20.1.6 Convergence Check
        • 20.1.7 LBFGS_SCALE_HESSIAN
        • 20.1.8 NNET_HELDASIDE_MAX_FAIL
      • 20.2 Data Preparation for Neural Network
      • 20.3 Neural Network Algorithm Configuration
      • 20.4 Scoring with Neural Network
    • 21 Non-Negative Matrix Factorization
      • 21.1 About NMF
        • 21.1.1 Matrix Factorization
        • 21.1.2 Scoring with NMF
        • 21.1.3 Text Mining with NMF
      • 21.2 Tuning the NMF Algorithm
      • 21.3 Data Preparation for NMF
    • 22 O-Cluster
      • 22.1 About O-Cluster
        • 22.1.1 Partitioning Strategy
          • 22.1.1.1 Partitioning Numerical Attributes
          • 22.1.1.2 Partitioning Categorical Attributes
        • 22.1.2 Active Sampling
        • 22.1.3 Process Flow
        • 22.1.4 Scoring
      • 22.2 Tuning the O-Cluster Algorithm
      • 22.3 Data Preparation for O-Cluster
        • 22.3.1 User-Specified Data Preparation for O-Cluster
    • 23 R Extensibility
      • 23.1 Oracle Data Mining with R Extensibility
      • 23.2 Scoring with R
      • 23.3 About Algorithm Meta Data Registration
        • 23.3.1 Algorithm Meta Data Registration
    • 24 Random Forest
      • 24.1 About Random Forest
      • 24.2 Building a Random Forest
      • 24.3 Scoring with Random Forest
    • 25 Singular Value Decomposition
      • 25.1 About Singular Value Decomposition
        • 25.1.1 Matrix Manipulation
        • 25.1.2 Low Rank Decomposition
        • 25.1.3 Scalability
      • 25.2 Configuring the Algorithm
        • 25.2.1 Model Size
        • 25.2.2 Performance
        • 25.2.3 PCA scoring
      • 25.3 Data Preparation for SVD
    • 26 Support Vector Machines
      • 26.1 About Support Vector Machines
        • 26.1.1 Advantages of SVM
        • 26.1.2 Advantages of SVM in Oracle Data Mining
          • 26.1.2.1 Usability
          • 26.1.2.2 Scalability
        • 26.1.3 Kernel-Based Learning
      • 26.2 Tuning an SVM Model
      • 26.3 Data Preparation for SVM
        • 26.3.1 Normalization
        • 26.3.2 SVM and Automatic Data Preparation
      • 26.4 SVM Classification
        • 26.4.1 Class Weights
      • 26.5 One-Class SVM
      • 26.6 SVM Regression
  • Part IV Using the Data Mining API
    • 27 Data Mining With SQL
      • 27.1 Highlights of the Data Mining API
      • 27.2 Example: Targeting Likely Candidates for a Sales Promotion
      • 27.3 Example: Analyzing Preferred Customers
      • 27.4 Example: Segmenting Customer Data
      • 27.5 Example : Building an ESA Model with a Wiki Dataset
    • 28 About the Data Mining API
      • 28.1 About Mining Models
      • 28.2 Data Mining Data Dictionary Views
        • 28.2.1 ALL_MINING_MODELS
        • 28.2.2 ALL_MINING_MODEL_ATTRIBUTES
        • 28.2.3 ALL_MINING_MODEL_PARTITIONS
        • 28.2.4 ALL_MINING_MODEL_SETTINGS
        • 28.2.5 ALL_MINING_MODEL_VIEWS
        • 28.2.6 ALL_MINING_MODEL_XFORMS
      • 28.3 Data Mining PL/SQL Packages
        • 28.3.1 DBMS_DATA_MINING
        • 28.3.2 DBMS_DATA_MINING_TRANSFORM
          • 28.3.2.1 Transformation Methods in DBMS_DATA_MINING_TRANSFORM
        • 28.3.3 DBMS_PREDICTIVE_ANALYTICS
      • 28.4 Data Mining SQL Scoring Functions
    • 29 Preparing the Data
      • 29.1 Data Requirements
        • 29.1.1 Column Data Types
        • 29.1.2 Data Sets for Classification and Regression
        • 29.1.3 Scoring Requirements
      • 29.2 About Attributes
        • 29.2.1 Data Attributes and Model Attributes
        • 29.2.2 Target Attribute
        • 29.2.3 Numericals, Categoricals, and Unstructured Text
        • 29.2.4 Model Signature
        • 29.2.5 Scoping of Model Attribute Name
        • 29.2.6 Model Details
      • 29.3 Using Nested Data
        • 29.3.1 Nested Object Types
        • 29.3.2 Example: Transforming Transactional Data for Mining
      • 29.4 Using Market Basket Data
        • 29.4.1 Example: Creating a Nested Column for Market Basket Analysis
      • 29.5 Using Retail Analysis Data
        • 29.5.1 Example: Calculating Aggregates
      • 29.6 Handling Missing Values
        • 29.6.1 Examples: Missing Values or Sparse Data?
          • 29.6.1.1 Sparsity in a Sales Table
          • 29.6.1.2 Missing Values in a Table of Customer Data
        • 29.6.2 Missing Value Treatment in Oracle Data Mining
        • 29.6.3 Changing the Missing Value Treatment
    • 30 Transforming the Data
      • 30.1 About Transformations
      • 30.2 Preparing the Case Table
        • 30.2.1 Creating Nested Columns
        • 30.2.2 Converting Column Data Types
        • 30.2.3 Text Transformation
        • 30.2.4 About Business and Domain-Sensitive Transformations
      • 30.3 Understanding Automatic Data Preparation
        • 30.3.1 Binning
        • 30.3.2 Normalization
        • 30.3.3 Outlier Treatment
        • 30.3.4 How ADP Transforms the Data
      • 30.4 Embedding Transformations in a Model
        • 30.4.1 Specifying Transformation Instructions for an Attribute
          • 30.4.1.1 Expression Records
          • 30.4.1.2 Attribute Specifications
        • 30.4.2 Building a Transformation List
          • 30.4.2.1 SET_TRANSFORM
          • 30.4.2.2 The STACK Interface
          • 30.4.2.3 GET_MODEL_TRANSFORMATIONS and GET_TRANSFORM_LIST
        • 30.4.3 Transformation Lists and Automatic Data Preparation
        • 30.4.4 Oracle Data Mining Transformation Routines
          • 30.4.4.1 Binning Routines
          • 30.4.4.2 Normalization Routines
          • 30.4.4.3 Routines for Outlier Treatment
      • 30.5 Understanding Reverse Transformations
    • 31 Creating a Model
      • 31.1 Before Creating a Model
      • 31.2 The CREATE_MODEL Procedure
        • 31.2.1 Choosing the Mining Function
        • 31.2.2 Choosing the Algorithm
        • 31.2.3 Supplying Transformations
          • 31.2.3.1 Creating a Transformation List
          • 31.2.3.2 Transformation List and Automatic Data Preparation
        • 31.2.4 About Partitioned Model
          • 31.2.4.1 Partitioned Model Build Process
          • 31.2.4.2 DDL in Partitioned model
            • 31.2.4.2.1 Drop Model or Drop Partition
            • 31.2.4.2.2 Add Partition
          • 31.2.4.3 Partitioned Model scoring
      • 31.3 Specifying Model Settings
        • 31.3.1 Specifying Costs
        • 31.3.2 Specifying Prior Probabilities
        • 31.3.3 Specifying Class Weights
        • 31.3.4 Model Settings in the Data Dictionary
        • 31.3.5 Specifying Mining Model Settings for R Model
          • 31.3.5.1 ALGO_EXTENSIBLE_LANG
          • 31.3.5.2 RALG_BUILD_FUNCTION
            • 31.3.5.2.1 RALG_BUILD_PARAMETER
          • 31.3.5.3 RALG_DETAILS_FUNCTION
            • 31.3.5.3.1 RALG_DETAILS_FORMAT
          • 31.3.5.4 RALG_SCORE_FUNCTION
          • 31.3.5.5 RALG_WEIGHT_FUNCTION
          • 31.3.5.6 Registered R Scripts
          • 31.3.5.7 R Model Demonstration Scripts
      • 31.4 Model Detail Views
        • 31.4.1 Model Detail Views for Association Rules
        • 31.4.2 Model Detail View for Frequent Itemsets
        • 31.4.3 Model Detail View for Transactional Itemsets
        • 31.4.4 Model Detail View for Transactional Rule
        • 31.4.5 Model Detail Views for Classification Algorithms
        • 31.4.6 Model Detail Views for Decision Tree
        • 31.4.7 Model Detail Views for Generalized Linear Model
        • 31.4.8 Model Detail Views for Naive Bayes
        • 31.4.9 Model Detail Views for Neural Network
        • 31.4.10 Model Detail Views for Random Forest
        • 31.4.11 Model Detail View for Support Vector Machine
        • 31.4.12 Model Detail Views for Clustering Algorithms
        • 31.4.13 Model Detail Views for Expectation Maximization
        • 31.4.14 Model Detail Views for k-Means
        • 31.4.15 Model Detail Views for O-Cluster
        • 31.4.16 Model Detail Views for CUR Matrix Decomposition
        • 31.4.17 Model Detail Views for Explicit Semantic Analysis
        • 31.4.18 Model Detail Views for Exponential Smoothing Models
        • 31.4.19 Model Detail Views for Non-Negative Matrix Factorization
        • 31.4.20 Model Detail Views for Singular Value Decomposition
        • 31.4.21 Model Detail View for Minimum Description Length
        • 31.4.22 Model Detail View for Binning
        • 31.4.23 Model Detail Views for Global Information
        • 31.4.24 Model Detail View for Normalization and Missing Value Handling
    • 32 Scoring and Deployment
      • 32.1 About Scoring and Deployment
      • 32.2 Using the Data Mining SQL Functions
        • 32.2.1 Choosing the Predictors
        • 32.2.2 Single-Record Scoring
      • 32.3 Prediction Details
        • 32.3.1 Cluster Details
        • 32.3.2 Feature Details
        • 32.3.3 Prediction Details
        • 32.3.4 GROUPING Hint
      • 32.4 Real-Time Scoring
      • 32.5 Dynamic Scoring
      • 32.6 Cost-Sensitive Decision Making
      • 32.7 DBMS_DATA_MINING.Apply
    • 33 Mining Unstructured Text
      • 33.1 About Unstructured Text
      • 33.2 About Text Mining and Oracle Text
      • 33.3 Data Preparation for Text Features
      • 33.4 Creating a Model that Includes Text Mining
      • 33.5 Creating a Text Policy
      • 33.6 Configuring a Text Attribute
    • 34 Administrative Tasks for Oracle Data Mining
      • 34.1 Installing and Configuring a Database for Data Mining
        • 34.1.1 About Installation
        • 34.1.2 Enabling or Disabling a Database Option
        • 34.1.3 Database Tuning Considerations for Data Mining
      • 34.2 Upgrading or Downgrading Oracle Data Mining
        • 34.2.1 Pre-Upgrade Steps
          • 34.2.1.1 Dropping Models Created in Java
          • 34.2.1.2 Dropping Mining Activities Created in Oracle Data Miner Classic
        • 34.2.2 Upgrading Oracle Data Mining
          • 34.2.2.1 Using Database Upgrade Assistant to Upgrade Oracle Data Mining
            • 34.2.2.1.1 Upgrading from Release 10g
            • 34.2.2.1.2 Upgrading from Release 11g
          • 34.2.2.2 Using Export/Import to Upgrade Data Mining Models
            • 34.2.2.2.1 Export/Import Release 10g Data Mining Models
            • 34.2.2.2.2 Export/Import Release 11g Data Mining Models
        • 34.2.3 Post Upgrade Steps
        • 34.2.4 Downgrading Oracle Data Mining
      • 34.3 Exporting and Importing Mining Models
        • 34.3.1 About Oracle Data Pump
        • 34.3.2 Options for Exporting and Importing Mining Models
        • 34.3.3 Directory Objects for EXPORT_MODEL and IMPORT_MODEL
        • 34.3.4 Using EXPORT_MODEL and IMPORT_MODEL
        • 34.3.5 EXPORT and IMPORT Serialized Models
        • 34.3.6 Importing From PMML
      • 34.4 Controlling Access to Mining Models and Data
        • 34.4.1 Creating a Data Mining User
          • 34.4.1.1 Granting Privileges for Data Mining
        • 34.4.2 System Privileges for Data Mining
        • 34.4.3 Object Privileges for Mining Models
      • 34.5 Auditing and Adding Comments to Mining Models
        • 34.5.1 Adding a Comment to a Mining Model
        • 34.5.2 Auditing Mining Models
    • 35 The Data Mining Sample Programs
      • 35.1 About the Data Mining Sample Programs
      • 35.2 Installing the Data Mining Sample Programs
      • 35.3 The Data Mining Sample Data
  • Part V Oracle Data Mining API Reference
    • 36 PL/SQL Packages
      • 36.1 DBMS_DATA_MINING
        • 36.1.1 Using DBMS_DATA_MINING
          • 36.1.1.1 DBMS_DATA_MINING Overview
          • 36.1.1.2 DBMS_DATA_MINING Security Model
          • 36.1.1.3 DBMS_DATA_MINING — Mining Functions
        • 36.1.2 DBMS_DATA_MINING — Model Settings
          • 36.1.2.1 DBMS_DATA_MINING — Algorithm Names
          • 36.1.2.2 DBMS_DATA_MINING — Automatic Data Preparation
          • 36.1.2.3 DBMS_DATA_MINING — Mining Function Settings
          • 36.1.2.4 DBMS_DATA_MINING — Global Settings
          • 36.1.2.5 DBMS_DATA_MINING — Algorithm Settings: ALGO_EXTENSIBLE_LANG
          • 36.1.2.6 DBMS_DATA_MINING — Algorithm Settings: CUR Matrix Decomposition
          • 36.1.2.7 DBMS_DATA_MINING — Algorithm Settings: Decision Tree
          • 36.1.2.8 DBMS_DATA_MINING — Algorithm Settings: Expectation Maximization
          • 36.1.2.9 DBMS_DATA_MINING — Algorithm Settings: Explicit Semantic Analysis
          • 36.1.2.10 DBMS_DATA_MINING — Algorithm Settings: Exponential Smoothing Models
          • 36.1.2.11 DBMS_DATA_MINING — Algorithm Settings: Generalized Linear Models
          • 36.1.2.12 DBMS_DATA_MINING — Algorithm Settings: k-Means
          • 36.1.2.13 DBMS_DATA_MINING — Algorithm Settings: Naive Bayes
          • 36.1.2.14 DBMS_DATA_MINING — Algorithm Settings: Neural Network
          • 36.1.2.15 DBMS_DATA_MINING — Algorithm Settings: Non-Negative Matrix Factorization
          • 36.1.2.16 DBMS_DATA_MINING — Algorithm Settings: O-Cluster
          • 36.1.2.17 DBMS_DATA_MINING — Algorithm Settings: Random Forest
          • 36.1.2.18 DBMS_DATA_MINING — Algorithm Constants and Settings: Singular Value Decomposition
          • 36.1.2.19 DBMS_DATA_MINING — Algorithm Settings: Support Vector Machine
        • 36.1.3 DBMS_DATA_MINING — Solver Settings
          • 36.1.3.1 DBMS_DATA_MINING — Solver Settings: ADMM
          • 36.1.3.2 DBMS_DATA_MINING — Solver Settings: LBFGS
        • 36.1.4 DBMS_DATA_MINING Datatypes
          • 36.1.4.1 Deprecated Types
        • 36.1.5 Summary of DBMS_DATA_MINING Subprograms
          • 36.1.5.1 ADD_COST_MATRIX Procedure
          • 36.1.5.2 ADD_PARTITION Procedure
          • 36.1.5.3 ALTER_REVERSE_EXPRESSION Procedure
          • 36.1.5.4 APPLY Procedure
          • 36.1.5.5 COMPUTE_CONFUSION_MATRIX Procedure
          • 36.1.5.6 COMPUTE_CONFUSION_MATRIX_PART Procedure
          • 36.1.5.7 COMPUTE_LIFT Procedure
          • 36.1.5.8 COMPUTE_LIFT_PART Procedure
          • 36.1.5.9 COMPUTE_ROC Procedure
          • 36.1.5.10 COMPUTE_ROC_PART Procedure
          • 36.1.5.11 CREATE_MODEL Procedure
          • 36.1.5.12 CREATE_MODEL2 Procedure
          • 36.1.5.13 Create Model Using Registration Information
          • 36.1.5.14 DROP_ALGORITHM Procedure
          • 36.1.5.15 DROP_PARTITION Procedure
          • 36.1.5.16 DROP_MODEL Procedure
          • 36.1.5.17 EXPORT_MODEL Procedure
          • 36.1.5.18 EXPORT_SERMODEL Procedure
          • 36.1.5.19 FETCH_JSON_SCHEMA Procedure
          • 36.1.5.20 GET_ASSOCIATION_RULES Function
          • 36.1.5.21 GET_FREQUENT_ITEMSETS Function
          • 36.1.5.22 GET_MODEL_COST_MATRIX Function
          • 36.1.5.23 GET_MODEL_DETAILS_AI Function
          • 36.1.5.24 GET_MODEL_DETAILS_EM Function
          • 36.1.5.25 GET_MODEL_DETAILS_EM_COMP Function
          • 36.1.5.26 GET_MODEL_DETAILS_EM_PROJ Function
          • 36.1.5.27 GET_MODEL_DETAILS_GLM Function
          • 36.1.5.28 GET_MODEL_DETAILS_GLOBAL Function
          • 36.1.5.29 GET_MODEL_DETAILS_KM Function
          • 36.1.5.30 GET_MODEL_DETAILS_NB Function
          • 36.1.5.31 GET_MODEL_DETAILS_NMF Function
          • 36.1.5.32 GET_MODEL_DETAILS_OC Function
          • 36.1.5.33 GET_MODEL_SETTINGS Function
          • 36.1.5.34 GET_MODEL_SIGNATURE Function
          • 36.1.5.35 GET_MODEL_DETAILS_SVD Function
          • 36.1.5.36 GET_MODEL_DETAILS_SVM Function
          • 36.1.5.37 GET_MODEL_DETAILS_XML Function
          • 36.1.5.38 GET_MODEL_TRANSFORMATIONS Function
          • 36.1.5.39 GET_TRANSFORM_LIST Procedure
          • 36.1.5.40 IMPORT_MODEL Procedure
          • 36.1.5.41 IMPORT_SERMODEL Procedure
          • 36.1.5.42 JSON Schema for R Extensible Algorithm
          • 36.1.5.43 REGISTER_ALGORITHM Procedure
          • 36.1.5.44 RANK_APPLY Procedure
          • 36.1.5.45 REMOVE_COST_MATRIX Procedure
          • 36.1.5.46 RENAME_MODEL Procedure
      • 36.2 DBMS_DATA_MINING_TRANSFORM
        • 36.2.1 Using DBMS_DATA_MINING_TRANSFORM
          • 36.2.1.1 DBMS_DATA_MINING_TRANSFORM Overview
          • 36.2.1.2 DBMS_DATA_MINING_TRANSFORM Security Model
          • 36.2.1.3 DBMS_DATA_MINING_TRANSFORM Datatypes
          • 36.2.1.4 DBMS_DATA_MINING_TRANSFORM Constants
        • 36.2.2 DBMS_DATA_MINING_TRANSFORM Operational Notes
          • 36.2.2.1 DBMS_DATA_MINING_TRANSFORM — About Transformation Lists
          • 36.2.2.2 DBMS_DATA_MINING_TRANSFORM — About Stacking and Stack Procedures
          • 36.2.2.3 DBMS_DATA_MINING_TRANSFORM — Nested Data Transformations
        • 36.2.3 Summary of DBMS_DATA_MINING_TRANSFORM Subprograms
          • 36.2.3.1 CREATE_BIN_CAT Procedure
          • 36.2.3.2 CREATE_BIN_NUM Procedure
          • 36.2.3.3 CREATE_CLIP Procedure
          • 36.2.3.4 CREATE_COL_REM Procedure
          • 36.2.3.5 CREATE_MISS_CAT Procedure
          • 36.2.3.6 CREATE_MISS_NUM Procedure
          • 36.2.3.7 CREATE_NORM_LIN Procedure
          • 36.2.3.8 DESCRIBE_STACK Procedure
          • 36.2.3.9 GET_EXPRESSION Function
          • 36.2.3.10 INSERT_AUTOBIN_NUM_EQWIDTH Procedure
          • 36.2.3.11 INSERT_BIN_CAT_FREQ Procedure
          • 36.2.3.12 INSERT_BIN_NUM_EQWIDTH Procedure
          • 36.2.3.13 INSERT_BIN_NUM_QTILE Procedure
          • 36.2.3.14 INSERT_BIN_SUPER Procedure
          • 36.2.3.15 INSERT_CLIP_TRIM_TAIL Procedure
          • 36.2.3.16 INSERT_CLIP_WINSOR_TAIL Procedure
          • 36.2.3.17 INSERT_MISS_CAT_MODE Procedure
          • 36.2.3.18 INSERT_MISS_NUM_MEAN Procedure
          • 36.2.3.19 INSERT_NORM_LIN_MINMAX Procedure
          • 36.2.3.20 INSERT_NORM_LIN_SCALE Procedure
          • 36.2.3.21 INSERT_NORM_LIN_ZSCORE Procedure
          • 36.2.3.22 SET_EXPRESSION Procedure
          • 36.2.3.23 SET_TRANSFORM Procedure
          • 36.2.3.24 STACK_BIN_CAT Procedure
          • 36.2.3.25 STACK_BIN_NUM Procedure
          • 36.2.3.26 STACK_CLIP Procedure
          • 36.2.3.27 STACK_COL_REM Procedure
          • 36.2.3.28 STACK_MISS_CAT Procedure
          • 36.2.3.29 STACK_MISS_NUM Procedure
          • 36.2.3.30 STACK_NORM_LIN Procedure
          • 36.2.3.31 XFORM_BIN_CAT Procedure
          • 36.2.3.32 XFORM_BIN_NUM Procedure
          • 36.2.3.33 XFORM_CLIP Procedure
          • 36.2.3.34 XFORM_COL_REM Procedure
          • 36.2.3.35 XFORM_EXPR_NUM Procedure
          • 36.2.3.36 XFORM_EXPR_STR Procedure
          • 36.2.3.37 XFORM_MISS_CAT Procedure
          • 36.2.3.38 XFORM_MISS_NUM Procedure
          • 36.2.3.39 XFORM_NORM_LIN Procedure
          • 36.2.3.40 XFORM_STACK Procedure
      • 36.3 DBMS_PREDICTIVE_ANALYTICS
        • 36.3.1 Using DBMS_PREDICTIVE_ANALYTICS
          • 36.3.1.1 DBMS_PREDICTIVE_ANALYTICS Overview
          • 36.3.1.2 DBMS_PREDICTIVE_ANALYTICS Security Model
        • 36.3.2 Summary of DBMS_PREDICTIVE_ANALYTICS Subprograms
          • 36.3.2.1 EXPLAIN Procedure
          • 36.3.2.2 PREDICT Procedure
          • 36.3.2.3 PROFILE Procedure
    • 37 Data Dictionary Views
      • 37.1 ALL_MINING_MODELS
      • 37.2 ALL_MINING_MODEL_ATTRIBUTES
      • 37.3 ALL_MINING_MODEL_PARTITIONS
      • 37.4 ALL_MINING_MODEL_SETTINGS
      • 37.5 ALL_MINING_MODEL_VIEWS
      • 37.6 ALL_MINING_MODEL_XFORMS
    • 38 SQL Scoring Functions
      • 38.1 CLUSTER_DETAILS
      • 38.2 CLUSTER_DISTANCE
      • 38.3 CLUSTER_ID
      • 38.4 CLUSTER_PROBABILITY
      • 38.5 CLUSTER_SET
      • 38.6 FEATURE_COMPARE
      • 38.7 FEATURE_DETAILS
      • 38.8 FEATURE_ID
      • 38.9 FEATURE_SET
      • 38.10 FEATURE_VALUE
      • 38.11 ORA_DM_PARTITION_NAME
      • 38.12 PREDICTION
      • 38.13 PREDICTION_BOUNDS
      • 38.14 PREDICTION_COST
      • 38.15 PREDICTION_DETAILS
      • 38.16 PREDICTION_PROBABILITY
      • 38.17 PREDICTION_SET

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  2. Using the Data Mining API
  3. Data Mining With SQL

27 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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