List of Tables
- 2-1 Data Dictionary Views for Oracle Data Mining
- 2-2 Data Mining PL/SQL Packages
- 2-3 DBMS_DATA_MINING_TRANSFORM Transformation Methods
- 2-4 Data Mining SQL Functions
- 3-1 Target Data Types
- 3-2 Grocery Store Data
- 3-3 Missing Value Treatment by Algorithm
- 4-1 Oracle Data Mining Algorithms With ADP
- 4-2 Fields in a Transformation Record for an Attribute
- 4-3 Binning Methods in DBMS_DATA_MINING_TRANSFORM
- 4-4 Normalization Methods in DBMS_DATA_MINING_TRANSFORM
- 4-5 Outlier Treatment Methods in DBMS_DATA_MINING_TRANSFORM
- 5-1 Preparation for Creating a Mining Model
- 5-2 Mining Model Functions
- 5-3 Data Mining Algorithms
- 5-4 Settings Table Required Columns
- 5-5 General Model Settings
- 5-6 Algorithm-Specific Model Settings
- 5-7 Cost Matrix Table Required Columns
- 5-8 Priors Table Required Columns
- 5-9 Class Weights Table Required Columns
- 5-10 ALL_MINING_MODEL_SETTINGS
- 5-11 Rule View Columns for Transactional Inputs
- 5-12 Rule View for 2-Dimensional Input
- 5-13 Global Detail for Association Rules
- 5-14 Frequent Itemsets View
- 5-15 Transactional Itemsets View
- 5-16 Transactional Rule View
- 5-17 Target Map View
- 5-18 Scoring Cost View
- 5-19 Attribute Importance and Rank View
- 5-20 Row Importance and Rank View
- 5-21 CUR Matrix Decomposition Statistics Information In Model Global View.
- 5-22 Split Information View
- 5-23 Node Statistics View
- 5-24 Node Description View
- 5-25 Cost Matrix View
- 5-26 Decision Tree Statistics Information In Model Global View
- 5-27 Model View for Linear and Logistic Regression Models
- 5-28 Row Diagnostic View for Linear Regression
- 5-29 Row Diagnostic View for Logistic Regression
- 5-30 Global Details for Linear Regression
- 5-31 Global Details for Logistic Regression
- 5-32 Prior View for Naive Bayes
- 5-33 Result View for Naive Bayes
- 5-34 Naive Bayes Statistics Information In Model Global View
- 5-35 Weights View
- 5-36 Neural Networks Statistics Information In Model Global View
- 5-37 Variable Importance Model View
- 5-38 Random Forest Statistics Information In Model Global View
- 5-39 Linear Coefficient View for Support Vector Machine
- 5-40 Support Vector Statistics Information In Model Global View
- 5-41 Cluster Description View for Clustering Algorithm
- 5-42 Attribute View for Clustering Algorithm
- 5-43 Histogram View for Clustering Algorithm
- 5-44 Rule View for Clustering Algorithm
- 5-45 Component View
- 5-46 Frequency Component View
- 5-47 2–Dimensional Attribute Ranking for Expectation Maximization
- 5-48 Kullback-Leibler Divergence for Expectation Maximization
- 5-49 Projection table for Expectation Maximization
- 5-50 Global Details for Expectation Maximization
- 5-51 Cluster Description for k-Means
- 5-52 Scoring View for k-Means
- 5-53 k–Means Statistics Information In Model Global View
- 5-54 Description View
- 5-55 Histogram Component View
- 5-56 O-Cluster Statistics Information In Model Global View
- 5-57 Explicit Semantic Analysis Matrix for Feature Extraction
- 5-58 Explicit Semantic Analysis Matrix for Classification
- 5-59 Explicit Semantic Analysis Features for Explicit Semantic Analysis
- 5-60 Explicit Semantic Analysis Statistics Information In Model Global View
- 5-61 Encoding H Matrix View for Non-Negative Matrix Factorization
- 5-62 Inverse H Matrix View for Non-Negative Matrix Factorization
- 5-63 Non-Negative Matrix Factorization Statistics Information In Model Global View
- 5-64 S Matrix View
- 5-65 Right-singular Vectors of Singular Value Decomposition
- 5-66 Left-singular Vectors of Singular Value Decomposition or Projection Data in Principal Components
- 5-67 Global Details for Singular Value Decomposition
- 5-68 Attribute Importance View for Minimum Description Length
- 5-69 Minimum Description Length Statistics Information In Model Global View
- 5-70 Model Details View for Binning
- 5-71 Global Statistics View
- 5-72 Alert View
- 5-73 Computed Settings View
- 5-74 Normalization and Missing Value Handling View
- 5-75 Exponential Smoothing Model Statistics Information In Model Global View
- 6-1 Sample Cost Matrix
- 6-2 APPLY Output Table
- 7-1 Text Feature View for Extracted Text Features
- 7-2 Column Data Types That May Contain Unstructured Text
- 7-3 Model Settings for Text
- 7-4 CTX_DDL.CREATE_POLICY Procedure Parameters
- 7-5 Attribute-Specific Text Transformation Instructions
- 8-1 Export and Import Options for Oracle Data Mining
- 8-2 System Privileges for Data Mining
- 8-3 Object Privileges for Mining Models
- A-1 System Privileges Granted by dmshgrants.sql to the Data Mining User
- A-2 The Data Mining Sample Data