 6.1 Basic Concepts  6.2 Decision Tree Induction  6.3 Bayes Classification Methods  6.4 Rule-Based Classification  6.5 Model Evaluation and Selection  6.6 Techniques to Improve Classification Accuracy

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）Lecture 04 Association Rules of Data Reasoning（FP-growth Algorithm）

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）Lecture 03 Regression Analysis（Logistic Regression）

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

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）量子降维算法

◼ Cluster Basics ◼ Clustering algorithms  Hierarchical clustering  k-means  Expectation-Maximization (EM) ◼ Cluster Validity  determining the number of clusters  clustering evaluation

电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）量子支持向量机（support vector machine, SVM）

同济大学：《大数据分析与数据挖掘 Big Data Analysis and Mining》课程教学资源（PPT课件讲稿）Evaluation & other classifiers

同济大学：《大数据分析与数据挖掘 Big Data Analysis and Mining》课程教学资源（PPT课件讲稿）Decision Tree