In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 that enables data analysts to work directly with data inside the database.
The Data Miner GUI provides intuitive tools that help you to explore the data graphically, build and evaluate multiple data mining models, apply Oracle Data Mining models to new data, and deploy Oracle Data Mining's predictions and insights throughout the enterprise. Oracle Data Miner's SQL APIs automatically mine Oracle data and deploy results in real-time. Because the data, models, and results remain in the Oracle Database, data movement is eliminated, security is maximized and information latency is minimized.

课程对象: Oracle数据库技术人员






            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 Mode
            Building the Model
            Examining Test Results
            Applying the Model
            Evaluating Results
       Deploying Data Mining Results
            Requirements for deployment
            Deployment Tasks
            Examining Deployment Options