10.1 Introduction 10.2 Empirical Studies and Statistical Inference 10.3 Important Features of Big Data 10.4 Big Data Analysis and Statistics 10.5 Machine Learning and Statistics 10.6 Conclusion

 Data Center Introduction  Data Center Network Architectures  Fat-Tree  VL2  DCell  BCube  FiConn

• Struct • Union • Alignment • Pointer – Declaration – referencing, deferencing – memory allocation, memory free • Buffer overflow • Suggested reading • Stack evaluation • Data movement • Set special data • Arithmetic operation • Comparison • Suggested reading

▪ Overview ▪ Data Warehousing ▪ Online Analytical Processing ▪ Data Mining

Research methods Qualitative Vs quantitative Understanding the relationship between objectives(research question) and variables is critical Information≠Data Information=data analysis Planning in advance is a must To include how data will be analyzed

Decision Support Systems Data Warehousing Data Mining Classification Association Rules Clustering

To develop a subclass from a superclass through inheritance (§8.2). To invoke the superclass’s constructors and methods using the super keyword (§8.3). To override methods in the subclass (§8.4). To explore the useful methods (equals(Object), hashCode(), toString(), finalize(), clone(), and getClass()) in the Object class (§8.5, §8.11 Optional). To comprehend polymorphism, dynamic binding, and generic programming (§8.6). To describe casting and explain why explicit downcasting is necessary (§8.7). To understand the effect of hiding data fields and static methods (§8.8 Optional). To restrict access to data and methods using the protected visibility modifier (§8.9). To declare constants, unmodifiable methods, and nonextendable class using the final modifier (§8.10). To initialize data using initialization blocks and distinguish between instance initialization and static initialization blocks (§8.12 Optioanl)

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）

 4.1 The basic concept of association rules  4.2 Low-dimensional binary association rules  4.3 Multi-level association rules  4.4 Multidimensional association rules  4.5 The Affinity analysis based on the association 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）Lecture 04 Association Rules of Data Reasoning（Apriori Algorithm、Improve of Apriori Algorithm）