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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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  1. API Guide
  2. Using the Data Mining API
  3. About the Data Mining API

28 About the Data Mining API

Overview of the Oracle Data Mining application programming interface (API) components.

  • About Mining Models

  • Data Mining Data Dictionary Views

  • Data Mining PL/SQL Packages

  • Data Mining SQL Scoring Functions

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