《商业数据挖掘》实验教学大纲.doc

《商业数据挖掘》实验教学大纲 2012年2月 课程名称（课程编码） 课程名称：商业数据挖掘（Business Data Mining） 课程编码： 课程性质 限修 学时与学分 计划内： 32学时, 2 个学分 实 验：计划内 16学时，计划外 24学时 先修课程 数据库系统原理、应用统计分析、数据结构 课程教学目的 《商业数据挖掘》课程是信息管理与信息系统专业学生的主要专业课程之一。该课程涉及的多个学科领域的知识，包括数据库技术、人工智能、机器学习、统计分析、高性能计算和数据可视化等等。要求本专业学生在面向商业数据分析与挖掘的背景下，详细了解并掌握各类数据挖掘算法和应用过程。本课程是一门基础技术类课程，同时兼顾基本理论学习。 适用学科及专业 信息管理与信息系统、市场营销、工商管理、物流管理 基本教学内容与学时安排 1、Introduction () Opening Case: Beer and Diaper 1.1 What Is Business Data Mining? 1.2 Motivating Challenges from Business 1.3 Business Data Mining Functionalities 1.4 Business Data Mining Algorithms Key Points： Motivation; What is Data Mining; Data Mining Data/ Functionalities 2、Data Warehouse and OLAP Technology () 4.1 What is Data Warehouse? 4.2 OLAP and Multidimensional Data Analysis 4.3 From Data Warehousing to Business Data Mining 4.4 Summary Key Points：Definition; Multidimensional Data Model; Architecture; Implementation Note: Just a quick review. ? 3、Data Pre-processing () 3.1 Why preprocess the data? 3.2 Descriptive data summarization 3.3 Data cleaning 3.4 Data integration and transformation 3.5 Data reduction 3.6 Discretization and concept hierarchy generation 3.7 Summary Key points：Data Cleaning; Data integration and transformation; Data Reduction; Discretization 4、Classification and Prediction () 4.1 What is classification? What is prediction? 4.2 Issues regarding classification and prediction 4.3 Classification by decision tree induction 4.4 Bayesian classification 4.5 Classification by back propagation 4.6 Support Vector Machines (SVM) 4.7 Prediction 4.8 Accuracy and error measures 4.9 Model selection 4.10 Summary Key Points：Definitions; Bayesian Classification ; Decision Trees Notes: More details on related algorithms. 5、Association Rules (6hrs) Re-review the story of diaper and beer 5.1 Basic concepts and a road