第一章 绪论

1.1 What is Data Mining?

1.2 How Data Mining Works in Financial Area?

第一章测验

第二章 数据

2.1 What is data(1)?

2.2 What is data(2)?

2.3 Data pre-processing(1)

2.4 Data pre-processing(2)

2.5 Similarity and Dissimilarity of data(1)

2.6 Similarity and Dissimilarity of data(2)

2.7 Similarity and Dissimilarity of data(3)

第二章测验

第三章 分类——决策树分类器

3.1 Classification-basic concept and idea

3.2 Introduction to Decision Tree Classifier

3.3 Methods for Expressing Attribute Test Conditions

3.4 Measures of Impurity

3.5 An Example to Compute a Decision Tree

第三章单元测验

第四章 分类——基于规则的分类器

4.1 Introduction to Rule-Based Classifier

4.2 Direct Method for Rule Extraction

4.3 An Example to Build Rule-Based Classifier

第四章单元测验

第五章 分类——朴素贝叶斯分类器

5.1 Introduction to Naive Bayes Classifier

5.2 An Example to Build Naive Bayes Classifier

第五章测验

第六章 分类——贝叶斯信念网络分类器

6.1 Introduction to Bayesian Belief Networks

6.2 An Example of Bayesian Belief Networks

第六章测验

第七章 拟合不足和过拟合

7.1 Underfitting and Overfitting (1)

7.2 Underfitting and Overfitting (2)

第七章测验

第八章 分类-模型评估和比较

8.1 Classification-Model Evaluation

8.2 Classification-Model Comparison

第八章测验

第九章 关联分析-Apriori算法

9.1 Association Analysis-basic concept and idea

9.2 Apriori Algorithm (1)-Introduction

9.3 Apriori Algorithm (2)-Candidate Generation & Pruning

9.4 Apriori Algorithm (3)-Hash Tree

9.5 Apriori Algorithm (4)-Rule Generation and Complexity

第九章测验

第十章 关联分析-FP树增长算法

10.1 FP Tree Algorithm-Introduction

10.2 FP Tree Algorithm-An Example

第十章测验

第十一章 关联分析-关联模式评估

11.1 Association Analysis-Evaluation (1)

11.2 Association Analysis-Evaluation (2)

第十一章测验

第十二章 聚类分析-k-means算法

12.1 Cluster Analysis-basic concept and idea

12.2 Introduction to K-means Algorithm

第十二章测验

第十三章 聚类分析-基本层次聚类算法

13.1 Introduction to Agglomerative Hierarchical Clustering Algorithm

13.2 An Example of Agglomerative Hierarchical Clustering Algorithm

第十三章测验

第十四章 聚类分析-簇评估

14.1 Unsupervised Cluster Evaluation

14.2 Supervised Cluster Evaluation

第十四章测验