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最新Mathematica使用手册（pdf），特别针对Mathematica7.0。零起点，英文版。 CHAPTER 1 Getting Started CHAPTER 2 Two-Dimensional Graphics CHAPTER 3 Getting Help CHAPTER 4 Odds and Ends CHAPTER 5 Functions CHAPTER 6 Three-Dimensional Graphics CHAPTER 7 Calculus CHAPTER 8 Solving Equations CHAPTER 9 Working with Data CHAPTER 10 Dynamical Systems and Fractals CHAPTER 11 Looking Good

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• 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 Preprocessing: An Overview ◼ Data Quality ◼ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary

Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary

◼ Data Preprocessing: An Overview ◼ Data Quality ◼ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary

Motivation: Why data mining? What is data mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary