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 Frequent Itemsets View
• 5-13 Transactional Itemsets View
• 5-14 Transactional Rule View
• 5-15 Target Map View
• 5-16 Scoring Cost View
• 5-17 Split Information View
• 5-18 Node Statistics View
• 5-19 Node Description View
• 5-20 Cost Matrix View
• 5-21 Model View for Linear and Logistic Regression Models
• 5-22 Row Diagnostic View for Linear Regression
• 5-23 Row Diagnostic View for Logistic Regression
• 5-24 Prior View for Naive Bayes
• 5-25 Result View for Naive Bayes
• 5-26 Linear Coefficient View for Support Vector Machine
• 5-27 Cluster Description View for Clustering Algorithm
• 5-28 Attribute View for Clustering Algorithm
• 5-29 Histogram View for Clustering Algorithm
• 5-30 Rule View for Clustering Algorithm
• 5-31 Component View
• 5-32 Frequency Component View
• 5-33 2–Dimensional Attribute Ranking for Expectation Maximization
• 5-34 Kullback-Leibler Divergence for Expectation Maximization
• 5-35 Projection table for Expectation Maximization
• 5-36 Cluster Description for k-Means
• 5-37 Scoring View for k-Means
• 5-38 Description View
• 5-39 Histogram Component View
• 5-40 Attribute View for Explicit Semantic Analysis
• 5-41 Feature View for Explicit Semantic Analysis
• 5-42 Encoding H Matrix View for Non-Negative Matrix Factorization
• 5-43 Inverse H Matrix View for Non-Negative Matrix Factorization
• 5-44 S Matrix View
• 5-45 Right-singular Vectors of Singular Value Decomposition
• 5-46 Left-singular Vectors of Singular Value Decomposition or Projection Data in Principal Components
• 5-47 Attribute Importance View for Minimum Description Length
• 5-48 Model Details View for Binning
• 5-49 Global Statistics View
• 5-50 Alert View
• 5-51 Computed Settings View
• 5-52 Normalization and Missing Value Handling 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