Contents

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Preface

1 Introduction to Oracle Data Mining

1.1 What is Data Mining?

1.2 What Is Oracle Data Mining?

1.2.1 Oracle Data Mining Programming Interfaces

1.2.2 ODM Data Mining Functions

2 Data for Oracle Data Mining

2.1 ODM Data, Cases, and Attributes

2.2 ODM Data Requirements

2.2.1 ODM Data Table Format

2.2.1.1 Single-Record Case Data

2.2.1.2 Multi-Record Case Data in the Java Interface

2.2.1.3 Wide Data in DBMS_DATA_MINING

2.2.2 Column Data Types Supported by ODM

2.2.2.1 Unstructured Data in ODM

2.2.2.2 Dates in ODM

2.2.3 Attribute Type for Oracle Data Mining

2.2.3.1 Target t Attribute

2.2.4 Data Storage Issues

2.2.5 Missing Values in ODM

2.2.5.1 Missing Values and Null Values in ODM

2.2.5.2 Missing Values Handling

2.2.6 Sparse Data in Oracle Data Mining

2.2.7 Outliers and Oracle Data Mining

2.3 Prepared and Unprepared Data

2.3.1 Data Preparation for the ODM Java Interface

2.3.2 Data Preparation for DBMS_DATA_MINING

2.3.3 Binning (Discretization) in Data Mining

2.3.3.1 Methods for Computing Bin Boundaries

2.3.4 Normalization in Oracle Data Mining

3 Predictive Data Mining Models

3.1 Classification

3.1.1 Costs

3.1.2 Priors

3.1.3 Naive Bayes Algorithm

3.1.4 Adaptive Bayes Network Algorithm

3.1.4.1 ABN Model Types

3.1.4.2 ABN Rules

3.1.4.3 ABN Build Parameters

3.1.4.4 ABN Model States

3.1.5 Comparison of NB and ABN Models

3.1.6 Support Vector Machine

3.1.6.1 Data Preparation and Settings Choice for Support Vector Machines

3.2 Regression

3.2.1 SVM Algorithm for Regression

3.3 Attribute Importance

3.3.1 Minimum Descriptor Length

3.4 ODM Model Seeker (Java Interface Only)

4 Descriptive Data Mining Models

4.1 Clustering in Oracle Data Mining

4.1.1 Enhanced k-Means Algorithm

4.1.1.1 Data for k-Means

4.1.1.2 Scalability through Summarization

4.1.1.3 Scoring (Applying Models)

4.1.2 Orthogonal Partitioning Clustering (O-Cluster)

4.1.2.1 O-Cluster Data Use

4.1.2.2 Binning for O-Cluster

4.1.2.3 O-Cluster Attribute Type

4.1.2.4 O-Cluster Scoring

4.1.3 K-Means and O-Cluster Comparison

4.2 Association Models in Oracle Data Mining

4.2.1 Finding Associations Involving Rare Events

4.2.2 Finding Associations in Dense Data Sets

4.2.3 Data for Association Models

4.2.4 Apriori Algorithm

4.3 Feature Extraction in Oracle Data Mining

4.3.1 Non-Negative Matrix Factorization

4.3.1.1 NMF for Text Mining

5 Data Mining Using the Java Interface

5.1 Building a Model

5.2 Testing a Model

5.2.1 Computing Lift

5.3 Applying a Model (Scoring)

5.4 Model Export and Import

6 Objects and Functionality in the Java Interface

6.1 Physical Data Specification

6.2 Mining Function Settings

6.3 Mining Algorithm Settings

6.4 Logical Data Specification

6.5 Mining Attributes

6.6 Data Usage Specification

6.6.1 ODM Attribute Names and Case

6.7 Mining Model

6.8 Mining Results

6.9 Confusion Matrix

6.10 Mining Apply Output

7 Data Mining Using DBMS_DATA_MINING

7.1 DBMS_DATA_MINING Application Development

7.2 Building DBMS_DATA_MINING Models

7.2.1 DBMS_DATA_MINING Models

7.2.2 DBMS_DATA_MINING Mining Functions

7.2.3 DBMS_DATA_MINING Mining Algorithms

7.2.4 DBMS_DATA_MINING Settings Table

7.2.4.1 DBMS_DATA_MINING Prior Probabilities Table

7.2.4.2 DBMS_DATA_MINING Cost Matrix Table

7.3 DBMS_DATA_MINING Mining Operations and Results

7.3.1 DBMS_DATA_MINING Build Results

7.3.2 DBMS_DATA_MINING Apply Results

7.3.3 Evaluating DBMS_DATA_MINING Classification Models

7.3.3.1 Confusion Matrix

7.3.3.2 Lift

7.3.3.3 Receiver Operating Characteristics

7.3.4 Test Results for DBMS_DATA_MINING Regression Models

7.3.4.1 Root Mean Square Error

7.3.4.2 Mean Absolute Error

7.4 DBMS_DATA_MINING Model Export and Import

8 Text Mining Using Oracle Data Mining

8.1 What Text Mining Is

8.1.1 Document Classification

8.1.2 Combining Text and Numerical Data

8.2 ODM Technologies Supporting Text Mining

8.2.1 Classification and Text Mining

8.2.2 Clustering and Text Mining

8.2.3 Feature Extraction and Text Mining

8.2.4 Association and Regression and Text Mining

8.3 Oracle Support for Text Mining

9 Oracle Data Mining Scoring Engine

9.1 Oracle Data Mining Scoring Engine Features

9.2 Data Mining Scoring Engine Installation

9.3 Scoring in Data Mining Applications

9.4 Moving Data Mining Models

9.4.1 PMML Export and Import

9.4.2 Native ODM Export and Import

9.5 Using the Oracle Data Mining Scoring Engine

10 Sequence Similarity Search and Alignment (BLAST)

10.1 Bioinformatics Sequence Search and Alignment

10.2 BLAST in the Oracle Database

10.3 Oracle Data Mining Sequence Search and Alignment Capabilities

A ODM Interface Comparison

A.1 Target Users of the ODM Interfaces

A.2 Feature Comparison of the ODM Interfaces

A.3 The ODM Interfaces in Different Programming Environments

Glossary

Index