Oracle Database 11g: Data Mining Techniques (D73528) – Outline
Detailed Course Outline
Introduction
- Course Objectives
- Suggested Course Pre-requisites
- Suggested Course Schedule
- Class Sample Schemas
- Practice and Solutions Structure
- Review location of additional resources (including ODM and SQL Developer documentation and online resources)
Overviewing Data Mining Concepts
- What is Data Mining?
- Why use Data Mining?
- Examples of Data Mining Applications
- Supervised Versus Unsupervised Learning
- Supported Data Mining Algorithms and Uses
Understanding the Data Mining Process
- Common Tasks in the Data Mining Process
Introducing Oracle Data Miner 11g Release 2
- Data mining with Oracle Database
- Introducing the SQL Developer interface
- Setting up Oracle Data Miner
- Accessing the Data Miner GUI
- Identifying Data Miner interface components
- Examining Data Miner Nodes
- Previewing Data Miner Workflows
Using Classification Models
- Reviewing Classification Models
- Adding a Data Source to the Workflow
- Using the Data Source Wizard
- Creating Classification Models
- Building the Models
- Examining Class Build Tabs
- Comparing the Models
- Selecting and Examining a Model
Using Regression Models
- Reviewing Regression Models
- Adding a Data Source to the Workflow
- Using the Data Source Wizard
- Performing Data Transformations
- Creating Regression Models
- Building the Models
- Comparing the Models
- Selecting a Model
Performing Market Basket Analysis
- What is Market Basket Analysis?
- Reviewing Association Rules
- Creating a New Workflow
- Adding a Data Source to th Workflow
- Creating an Association Rules Model
- Defining Association Rules
- Building the Model
- Examining Test Results
Using Clustering Models
- Describing Algorithms used for Clustering Models
- Adding Data Sources to the Workflow
- Exploring Data for Patterns
- Defining and Building Clustering Models
- Comparing Model Results
- Selecting and Applying a Model
- Defining Output Format
- Examining Cluster Results
Performing Anomaly Detection
- Reviewing the Model and Algorithm used for Anomaly Detection
- Adding Data Sources to the Workflow
- Creating the Model
- Building the Model
- Examining Test Results
- Applying the Model
- Evaluating Results
Deploying Data Mining Results
- Requirements for deployment
- Deployment Tasks
- Examining Deployment Options