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

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

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

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

广东财经大学：智能财会管理学院《数据采集与挖掘》课程教学大纲

广东财经大学：信息学院《大数据分析与挖掘》实验课程教学大纲

广东财经大学：信息学院《大数据分析与挖掘》课程教学大纲

 What is data stream?  What is Concept Drift?  Data stream classification  Data stream clustering

电子科技大学：《大数据分析与挖掘 Big Data Analysis and Mining》课程教学资源（课件讲稿）Lecture 7 Hadoop-Spark

Key Node Identification Community Detection