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

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

Locality-Sensitive Hashing Find Similar Items CASE STUDY Finding Similar Documents Min-Hashing Locality-Sensitive Hashing Learn to Hash

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 1 Intro（主讲：邵俊明）

 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 04 Association Rules of Data Reasoning（Apriori Algorithm、Improve of Apriori Algorithm）

 3.1 Learning Problems  3.2 The least square method (LSM)  3.3 Linear regression analysis  1 Simple Linear Regression  2 Multiple Regression  3 Understanding the Regression Output  4 Coefficient of Determination R2  5 Validating the Regression Model

 2.1 Overview of data types  2.2 Review of Data pre-processing tools and platforms  2.3 Clean, storage and management of raw data  2.4 Collections of data analysis and data mining

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