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上课地点：【上海】：同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】：电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】：北京中山学院/福鑫大楼 【南京分部】：金港大厦(和燕路) 【武汉分部】：佳源大厦（高新二路） 【成都分部】：领馆区1号（中和大道） 【广州分部】：广粮大厦 【西安分部】：协同大厦 【沈阳分部】：沈阳理工大学/六宅臻品 【郑州分部】：郑州大学/锦华大厦 【石家庄分部】：河北科技大学/瑞景大厦
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| 课时 |
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| 质量以及保障 |
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☆课程大纲☆ |
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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
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