 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）

1. to know the transmission media of data signals, the structure of full-duplex data transmission system and its peration and the coding of data signals 2. to understand the main idea and

5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis

Introduction Examines a de facto standard for external data representation and presentation as well as a set of library procedures used to perform data conversion Describes the general motivations for using an external data representation and the details of one particular implementation

Individuals are the objects described by a set of data. Individuals may be people, but they may also be animals Relationship or things Between Variables A variable is any characteristic of an individual. a variable can take different values for different ndividuals The 1997 survey data set, for example, includes data about a sample of women The individuals described are the women

一、数据及数据文件(Data and Data file) 1.数据类型:(Data mode)用于编程和计算的数据类型(表31) 数组: 字符数组(Character array) 数值数组(Numeric array) 包括整形(int,uint8,int16,uint16,int32,uint32)单精度(signal),双精度(duble)(MATLAB最常用的变量类型),稀疏(sparce)数组

1 The Role of Algorithms in Computing 2 Getting Started 3 Growth of Functions 4 Divide-and-Conquer 5 Probabilistic Analysis and Randomized Algorithms 6 Heapsort 7 Quicksort 8 Sorting in Linear Time 9 Medians and Order Statistics 10 Elementary Data Structures 11 Hash Tables 12 Binary Search Trees 13 Red-Black Trees 14 Augmenting Data Structures 15 Dynamic Programming 16 Greedy Algorithms 17 Amortized Analysis 18 B-Trees 19 Fibonacci Heaps 20 van Emde Boas Trees 21 Data Structures for Disjoint Sets 22 Elementary Graph Algorithms 23 Minimum Spanning Trees 24 Single-Source Shortest Paths 25 All-Pairs Shortest Paths 26 Maximum Flow 27 Multithreaded Algorithms 28 Matrix Operations 29 Linear Programming 30 Polynomials and the FFT 31 Number-Theoretic Algorithms 32 String Matching 33 Computational Geometry 34 NP-Completeness 35 Approximation Algorithms

1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state—analytics/data science 1.3 Describe the business intelligence (BI) methodology and concepts 1.4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities

Chapter 3 Data types, Operators, and Expressions Preparations Data types OConstants and Variables Type Conversions OOperators, and Expressions