课程介绍： 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 课程大纲：         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 Mode             Building the Model             Examining Test Results             Applying the Model             Evaluating Results        Deploying Data Mining Results             Requirements for deployment             Deployment Tasks             Examining Deployment Options