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Oracle Database 11g: Data Mining Techniques (D73528)

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
 

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