• Data Mining – From data warehousing to data mining. – Data pre-processing and data mining life-cycle. – Association and sequence analysis; classification and clustering. – Fuzzy Logic, Neural Networks, and Genetic Algorithms. – Mining Complex Data. • OLAP mining; spatial data mining; text mining; time-series data mining; web mining; visual data mining. • Data warehousing. – Introduction; basic concepts of data warehousing; data warehouse vs. Operational DB; data warehouse and the industry. – Architecture and design; two-tier and three￾tier architecture; star schema and snowflake schema; data capturing, replication, transformation and cleansing. – Data characteristics; metadata; static and dynamic data; derived data. – Data Marts; OLAP; data mining; data warehouse administration

◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Warehouse Design and Usage ◼ Data Warehouse Implementation ◼ Data Generalization by Attribute-Oriented Induction ◼ Summary

数据仓库一数据挖掘的有效平台 数据仓库中的数据清理和数据集成,是数据挖掘的重要数据预处理步骤，数据仓库提供OLAP工具,可用于不同粒度的数据分析

Accessing SQL From a Programming Language Functions and Procedural Constructs Triggers Recursive Queries Advanced Aggregation Features OLAP

Decision Support Systems Data Analysis and OLAP Data Warehousing Data Mining

◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Cube Computation: Preliminary Concepts ◼ Data Cube Computation Methods ◼ Summary

◼ 商务智能概述 ◼ 在线分析处理OLAP技术 ◼ 数据挖掘技术和方法* ◼ 数据挖掘过程 ◼ 数据仓库 ◼ 商务智能应用*

◼ 13.1 从数据库到数据仓库 ◼ 13.2 数据仓库的结构 ◼ 13.3 数据仓库的设计 ◼ 13.4 OLAP与数据仓库 ◼ 13.5 数据挖掘（data mining，DM） ◼ 13.6 决策支持系统与数据仓库 ◼ 13.7 SQL Server 2000的数据仓库 ◼ 13.8 小结 ◼ 13.9 习题

第11章数据仓库和数据挖掘 1.数据仓库 2.联机分析处理(OLAP) 3.知识发现和数据挖掘