Table of Contents
- Title and Copyright Information
- Preface
-
Part I Introductions
- 1 Introduction to Oracle Data Mining
- 2 Oracle Data Mining Basics
-
Part II Mining Functions
- 3 Regression
- 4 Classification
- 5 Anomaly Detection
- 6 Clustering
- 7 Association
- 8 Feature Selection and Extraction
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Part III Algorithms
-
9
Apriori
- 9.1 About Apriori
- 9.2 Association Rules and Frequent Itemsets
- 9.3 Data Preparation for Apriori
-
9.4
Calculating Association Rules
- 9.4.1 Itemsets
- 9.4.2 Frequent Itemsets
- 9.4.3 Example: Calculating Rules from Frequent Itemsets
- 9.4.4 Aggregates
- 9.4.5 Reverse Confidence
- 9.4.6 Minimum Support Count
- 9.4.7 Transaction Count
- 9.4.8 Including and Excluding Rules
- 9.4.9 Excluding Rules
- 9.4.10 Example: Calculating Aggregates
- 9.4.11 Performance Impact for Aggregates
- 9.5 Evaluating Association Rules
- 10 Decision Tree
- 11 Expectation Maximization
- 12 Explicit Semantic Analysis
- 13 Generalized Linear Models
- 14 k-Means
- 15 Minimum Description Length
- 16 Naive Bayes
- 17 Non-Negative Matrix Factorization
- 18 O-Cluster
- 19 Singular Value Decomposition
- 20 Support Vector Machines
-
9
Apriori
-
Part IV Using the Data Mining API
- 21 Data Mining With SQL
- 22 About the Data Mining API
- 23 Preparing the Data
-
24
Transforming the Data
- 24.1 About Transformations
- 24.2 Preparing the Case Table
- 24.3 Understanding Automatic Data Preparation
- 24.4 Embedding Transformations in a Model
- 24.5 Understanding Reverse Transformations
-
25
Creating a Model
- 25.1 Before Creating a Model
- 25.2 The CREATE_MODEL Procedure
- 25.3 Specifying Model Settings
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25.4
Model Detail Views
- 25.4.1 Model Detail Views for Association Rules
- 25.4.2 Model Detail View for Frequent Itemsets
- 25.4.3 Model Detail View for Transactional Itemsets
- 25.4.4 Model Detail View for Transactional Rule
- 25.4.5 Model Detail Views for Classification Algorithms
- 25.4.6 Model Detail Views for Decision Tree
- 25.4.7 Model Detail Views for Generalized Linear Model
- 25.4.8 Model Detail Views for Naive Bayes
- 25.4.9 Model Detail View for Support Vector Machine
- 25.4.10 Model Detail Views for Clustering Algorithms
- 25.4.11 Model Detail Views for Expectation Maximization
- 25.4.12 Model Detail Views for k-Means
- 25.4.13 Model Detail Views for O-Cluster
- 25.4.14 Model Detail Views for Explicit Semantic Analysis
- 25.4.15 Model Detail Views for Non-Negative Matrix Factorization
- 25.4.16 Model Detail Views for Singular Value Decomposition
- 25.4.17 Model Detail View for Minimum Description Length
- 25.4.18 Model Detail View for Binning
- 25.4.19 Model Detail Views for Global Information
- 25.4.20 Model Detail View for Normalization and Missing Value Handling
- 26 Scoring and Deployment
- 27 Mining Unstructured Text
-
28
Administrative Tasks for Oracle Data Mining
- 28.1 Installing and Configuring a Database for Data Mining
- 28.2 Upgrading or Downgrading Oracle Data Mining
- 28.3 Exporting and Importing Mining Models
- 28.4 Controlling Access to Mining Models and Data
- 28.5 Auditing and Adding Comments to Mining Models
- 29 The Data Mining Sample Programs
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Part V Oracle Data Mining API Reference
-
30
PL/SQL Packages
-
30.1
DBMS_DATA_MINING
- 30.1.1 Using DBMS_DATA_MINING
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30.1.2
DBMS_DATA_MINING — Model Settings
- 30.1.2.1 DBMS_DATA_MINING — Algorithm Names
- 30.1.2.2 DBMS_DATA_MINING — Automatic Data Preparation
- 30.1.2.3 DBMS_DATA_MINING — Mining Function Settings
- 30.1.2.4 DBMS_DATA_MINING — Global Settings
- 30.1.2.5 DBMS_DATA_MINING — Algorithm Settings: ALGO_EXTENSIBLE_LANG
- 30.1.2.6 DBMS_DATA_MINING — Algorithm Settings: Decision Tree
- 30.1.2.7 DBMS_DATA_MINING — Algorithm Settings: Expectation Maximization
- 30.1.2.8 DBMS_DATA_MINING — Algorithm Settings: Explicit Semantic Analysis
- 30.1.2.9 DBMS_DATA_MINING — Algorithm Settings: Generalized Linear Models
- 30.1.2.10 DBMS_DATA_MINING — Algorithm Settings: k-Means
- 30.1.2.11 DBMS_DATA_MINING — Algorithm Settings: Naive Bayes
- 30.1.2.12 DBMS_DATA_MINING — Algorithm Settings: Non-Negative Matrix Factorization
- 30.1.2.13 DBMS_DATA_MINING — Algorithm Settings: O-Cluster
- 30.1.2.14 DBMS_DATA_MINING — Algorithm Constants and Settings: Singular Value Decomposition
- 30.1.2.15 DBMS_DATA_MINING — Algorithm Settings: Support Vector Machine
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30.1.3
Summary of DBMS_DATA_MINING Subprograms
- 30.1.3.1 ADD_COST_MATRIX Procedure
- 30.1.3.2 ADD_PARTITION Procedure
- 30.1.3.3 ALTER_REVERSE_EXPRESSION Procedure
- 30.1.3.4 APPLY Procedure
- 30.1.3.5 COMPUTE_CONFUSION_MATRIX Procedure
- 30.1.3.6 COMPUTE_CONFUSION_MATRIX_PART Procedure
- 30.1.3.7 COMPUTE_LIFT Procedure
- 30.1.3.8 COMPUTE_LIFT_PART Procedure
- 30.1.3.9 COMPUTE_ROC Procedure
- 30.1.3.10 COMPUTE_ROC_PART Procedure
- 30.1.3.11 CREATE_MODEL Procedure
- 30.1.3.12 CREATE_MODEL2 Procedure
- 30.1.3.13 DROP_PARTITION Procedure
- 30.1.3.14 DROP_MODEL Procedure
- 30.1.3.15 EXPORT_MODEL Procedure
- 30.1.3.16 GET_ASSOCIATION_RULES Function
- 30.1.3.17 GET_FREQUENT_ITEMSETS Function
- 30.1.3.18 GET_MODEL_COST_MATRIX Function
- 30.1.3.19 GET_MODEL_DETAILS_AI Function
- 30.1.3.20 GET_MODEL_DETAILS_EM Function
- 30.1.3.21 GET_MODEL_DETAILS_EM_COMP Function
- 30.1.3.22 GET_MODEL_DETAILS_EM_PROJ Function
- 30.1.3.23 GET_MODEL_DETAILS_GLM Function
- 30.1.3.24 GET_MODEL_DETAILS_GLOBAL Function
- 30.1.3.25 GET_MODEL_DETAILS_KM Function
- 30.1.3.26 GET_MODEL_DETAILS_NB Function
- 30.1.3.27 GET_MODEL_DETAILS_NMF Function
- 30.1.3.28 GET_MODEL_DETAILS_OC Function
- 30.1.3.29 GET_MODEL_SETTINGS Function
- 30.1.3.30 GET_MODEL_SIGNATURE Function
- 30.1.3.31 GET_MODEL_DETAILS_SVD Function
- 30.1.3.32 GET_MODEL_DETAILS_SVM Function
- 30.1.3.33 GET_MODEL_DETAILS_XML Function
- 30.1.3.34 GET_MODEL_TRANSFORMATIONS Function
- 30.1.3.35 GET_TRANSFORM_LIST Procedure
- 30.1.3.36 IMPORT_MODEL Procedure
- 30.1.3.37 RANK_APPLY Procedure
- 30.1.3.38 REMOVE_COST_MATRIX Procedure
- 30.1.3.39 RENAME_MODEL Procedure
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30.2
DBMS_DATA_MINING_TRANSFORM
- 30.2.1 Using DBMS_DATA_MINING_TRANSFORM
- 30.2.2 DBMS_DATA_MINING_TRANSFORM Operational Notes
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30.2.3
Summary of DBMS_DATA_MINING_TRANSFORM Subprograms
- 30.2.3.1 CREATE_BIN_CAT Procedure
- 30.2.3.2 CREATE_BIN_NUM Procedure
- 30.2.3.3 CREATE_CLIP Procedure
- 30.2.3.4 CREATE_COL_REM Procedure
- 30.2.3.5 CREATE_MISS_CAT Procedure
- 30.2.3.6 CREATE_MISS_NUM Procedure
- 30.2.3.7 CREATE_NORM_LIN Procedure
- 30.2.3.8 DESCRIBE_STACK Procedure
- 30.2.3.9 GET_EXPRESSION Function
- 30.2.3.10 INSERT_AUTOBIN_NUM_EQWIDTH Procedure
- 30.2.3.11 INSERT_BIN_CAT_FREQ Procedure
- 30.2.3.12 INSERT_BIN_NUM_EQWIDTH Procedure
- 30.2.3.13 INSERT_BIN_NUM_QTILE Procedure
- 30.2.3.14 INSERT_BIN_SUPER Procedure
- 30.2.3.15 INSERT_CLIP_TRIM_TAIL Procedure
- 30.2.3.16 INSERT_CLIP_WINSOR_TAIL Procedure
- 30.2.3.17 INSERT_MISS_CAT_MODE Procedure
- 30.2.3.18 INSERT_MISS_NUM_MEAN Procedure
- 30.2.3.19 INSERT_NORM_LIN_MINMAX Procedure
- 30.2.3.20 INSERT_NORM_LIN_SCALE Procedure
- 30.2.3.21 INSERT_NORM_LIN_ZSCORE Procedure
- 30.2.3.22 SET_EXPRESSION Procedure
- 30.2.3.23 SET_TRANSFORM Procedure
- 30.2.3.24 STACK_BIN_CAT Procedure
- 30.2.3.25 STACK_BIN_NUM Procedure
- 30.2.3.26 STACK_CLIP Procedure
- 30.2.3.27 STACK_COL_REM Procedure
- 30.2.3.28 STACK_MISS_CAT Procedure
- 30.2.3.29 STACK_MISS_NUM Procedure
- 30.2.3.30 STACK_NORM_LIN Procedure
- 30.2.3.31 XFORM_BIN_CAT Procedure
- 30.2.3.32 XFORM_BIN_NUM Procedure
- 30.2.3.33 XFORM_CLIP Procedure
- 30.2.3.34 XFORM_COL_REM Procedure
- 30.2.3.35 XFORM_EXPR_NUM Procedure
- 30.2.3.36 XFORM_EXPR_STR Procedure
- 30.2.3.37 XFORM_MISS_CAT Procedure
- 30.2.3.38 XFORM_MISS_NUM Procedure
- 30.2.3.39 XFORM_NORM_LIN Procedure
- 30.2.3.40 XFORM_STACK Procedure
- 30.3 DBMS_PREDICTIVE_ANALYTICS
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30.1
DBMS_DATA_MINING
- 31 Data Dictionary Views
-
32
SQL Scoring Functions
- 32.1 CLUSTER_DETAILS
- 32.2 CLUSTER_DISTANCE
- 32.3 CLUSTER_ID
- 32.4 CLUSTER_PROBABILITY
- 32.5 CLUSTER_SET
- 32.6 FEATURE_COMPARE
- 32.7 FEATURE_DETAILS
- 32.8 FEATURE_ID
- 32.9 FEATURE_SET
- 32.10 FEATURE_VALUE
- 32.11 ORA_DM_PARTITION_NAME
- 32.12 PREDICTION
- 32.13 PREDICTION_BOUNDS
- 32.14 PREDICTION_COST
- 32.15 PREDICTION_DETAILS
- 32.16 PREDICTION_PROBABILITY
- 32.17 PREDICTION_SET
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30
PL/SQL Packages