◼ Cluster Analysis: Basic Concepts ◼ Partitioning Methods ◼ Hierarchical Methods ◼ Density-Based Methods ◼ Grid-Based Methods ◼ Evaluation of Clustering ◼ Summary

◼ Classification: Basic Concepts ◼ Decision Tree Induction ◼ Bayes Classification Methods ◼ Rule-Based Classification ◼ Model Evaluation and Selection ◼ Summary

◼ Pattern Mining: A Road Map ◼ Pattern Mining in Multi-Level, Multi-Dimensional Space ◼ Constraint-Based Frequent Pattern Mining ◼ Mining High-Dimensional Data and Colossal Patterns ◼ Mining Compressed or Approximate Patterns ◼ Pattern Exploration and Application ◼ Summary

◼ Basic Concepts ◼ Frequent Itemset Mining Methods ◼ Which Patterns Are Interesting?—Pattern Evaluation Methods ◼ Summary

◼ 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

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

◼ Data Objects and Attribute Types ◼ Basic Statistical Descriptions of Data ◼ Data Visualization ◼ Measuring Data Similarity and Dissimilarity ◼ 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

3.1 分析化学中的误差 3.2 有效数字及其运算规则 3.3 分析化学中的数据处理 3.4 显著性检验 3.5 可疑值取舍 3.6 回归分析法 3.7 提高分析结果准确度的方法

教学目的: 1. 掌握系统误差与随机误差的区别和减免；准确度与精密度的区别、联系与表示方法。 2. 熟练掌握有效数字的位数确定及运算规则，会用置信区间和置信概率处理分析数据。 3. 了解随机误差的分布规律，了解ｔ检验和 F 检验在具体分析中的应用。 教学重点： 1. 有效数字及其运算，标准偏差和平均值置信区间的计算 2. t、F 检验法的方法与作用，可疑值的取舍， 3. 提高分析结果准确度的方法。 教学难点： 1. 正态分布的概率范围； 2. 平均值的置信区间(如从σ求µ的置信区间,从 S 求 X 的置信区间，t 分布)