关于此课程： 这这一课程中，我们将学习数据挖掘的基本概念及其基础的方法和应用，然后深入到数据挖掘的子领域——模式发现中，学习模式发现深入的概念、方法，及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会，将可扩展的模式发现方法应用在在大体量交易数据上，讨论模式评估指标，以及学习用于挖掘各类不同的模式、序列模式，以及子图模式的方法。
制作方： University of Illinois at Urbana-Champaign
教学方： Jiawei Han, Abel Bliss Professor
Department of Computer Science
| 基本信息 | 课程 4（共 6 门，Data Mining Specialization ） |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 个视频, 3 个阅读材料, 1 个练习测验
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns.
9 个视频, 2 个阅读材料
- Reading: Lesson 1 Overview
- 视频: 1.2. Frequent Patterns and Association Rules
- 视频: 1.3. Compressed Representation: Closed Patterns and Max-Patterns
- Reading: Lesson 2 Overview
- 视频: 2.1. The Downward Closure Property of Frequent Patterns
- 视频: 2.2. The Apriori Algorithm
- 视频: 2.3. Extensions or Improvements of Apriori
- 视频: 2.4. Mining Frequent Patterns by Exploring Vertical Data Format
- 视频: 2.5. FPGrowth: A Pattern Growth Approach
- 视频: 2.6. Mining Closed Patterns
已评分: Lesson 1 Quiz
已评分: Lesson 2 Quiz
已评分: Frequent Itemset Mining Using Apriori
第 2 周
Module 2
Module 2 covers two lessons: Lessons 3 and 4. In Lesson 3, we discuss pattern evaluation and learn what kind of interesting measures should be used in pattern analysis. We show that the support-confidence framework is inadequate for pattern evaluation, and even the popularly used lift and chi-square measures may not be good under certain situations. We introduce the concept of null-invariance and introduce a new null-invariant measure for pattern evaluation. In Lesson 4, we examine the issues on mining a diverse spectrum of patterns. We learn the concepts of and mining methods for multiple-level associations, multi-dimensional associations, quantitative associations, negative correlations, compressed patterns, and redundancy-aware patterns.
9 个视频, 2 个阅读材料
- Reading: Lesson 3 Overview
- 视频: 3.2. Interestingness Measures: Lift and χ2
- 视频: 3.3. Null Invariance Measures
- 视频: 3.4. Comparison of Null-Invariant Measures
- Reading: Lesson 4 Overview
- 视频: 4.1. Mining Multi-Level Associations
- 视频: 4.2. Mining Multi-Dimensional Associations
- 视频: 4.3. Mining Quantitative Associations
- 视频: 4.4. Mining Negative Correlations
- 视频: 4.5. Mining Compressed Patterns
已评分: Lesson 3 Quiz
已评分: Lesson 4 Quiz
第 3 周
Module 3
Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns.
10 个视频, 2 个阅读材料
- Reading: Lesson 5 Overview
- 视频: 5.2. GSP: Apriori-Based Sequential Pattern Mining
- 视频: 5.3. SPADE—Sequential Pattern Mining in Vertical Data Format
- 视频: 5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth
- 视频: 5.5. CloSpan—Mining Closed Sequential Patterns
- Reading: Lesson 6 Overview
- 视频: 6.1. Mining Spatial Associations
- 视频: 6.2. Mining Spatial Colocation Patterns
- 视频: 6.3. Mining and Aggregating Patterns over Multiple Trajectories
- 视频: 6.4. Mining Semantics-Rich Movement Patterns
- 视频: 6.5. Mining Periodic Movement Patterns
已评分: Lesson 5 Quiz
已评分: Lesson 6 Quiz
第 4 周
Week 4
Module 4 consists of two lessons: Lessons 7 and 8. In Lesson 7, we study mining quality phrases from text data as the second kind of pattern mining application. We will mainly introduce two newer methods for phrase mining: ToPMine and SegPhrase, and show frequent pattern mining may be an important role for mining quality phrases in massive text data. In Lesson 8, we will learn several advanced topics on pattern discovery, including mining frequent patterns in data streams, pattern discovery for software bug mining, pattern discovery for image analysis, and pattern discovery and society: privacy-preserving pattern mining. Finally, we look forward to the future of pattern mining research and application exploration.
9 个视频, 2 个阅读材料
- Reading: Lesson 7 Overview
- 视频: 7.2. Previous Phrase Mining Methods
- 视频: 7.3. ToPMine: Phrase Mining without Training Data
- 视频: 7.4. SegPhrase: Phrase Mining with Tiny Training Sets
- Ungraded Programming: Mining Contiguous Sequential Patterns in Text
- Reading: Lesson 8 Overview
- 视频: 8.1. Frequent Pattern Mining in Data Streams
- 视频: 8.2. Pattern Discovery for Software Bug Mining
- 视频: 8.3. Pattern Discovery for Image Analysis
- 视频: 8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue
- 视频: 8.5. Advanced Topics on Pattern Discovery: Looking Forward
已评分: Lesson 7 Quiz
已评分: Lesson 8 Quiz
常见问题解答
运作方式
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制作方
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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评分和审阅
Very usefull course. I learned too much about pattern discovering.
Gave a very good introduction of Pattern Discovery and different mechanisms/algorithms for pattern discovery. Talks about different pattern discovery approaches, pros & cons of each. Found it very helpful for my investment analysis project
Very well-organised course. I especially liked the assignments: programming assignments were helpful to apply the course (this makes you implement the methods you learn), so were multiple-choice questions that really make you think on the course content. The instructor was clear and provided good materials. I would recommend this course.
IW
compact class which teaches you lots of knowledge without wasting any time, using frequent tests to renew your memory and test your comprehension.
The programming assignment is a little bit challenging though. I would like to post a guide if I figure out how to get 100/100.