Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
提供方
教学大纲 - 您将从这门课程中学到什么
周
1Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 个视频 （总计 7 分钟）, 3 个阅读材料, 1 个测验
1 个视频
3 个阅读材料
Syllabus10分钟
About the Discussion Forums10分钟
Social Media10分钟
1 个练习
Orientation Quiz10分钟
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 （总计 49 分钟）, 2 个阅读材料, 3 个测验
9 个视频
1.2. Frequent Patterns and Association Rules5分钟
1.3. Compressed Representation: Closed Patterns and Max-Patterns7分钟
2.1. The Downward Closure Property of Frequent Patterns3分钟
2.2. The Apriori Algorithm6分钟
2.3. Extensions or Improvements of Apriori7分钟
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3分钟
2.5. FPGrowth: A Pattern Growth Approach8分钟
2.6. Mining Closed Patterns3分钟
2 个阅读材料
Lesson 1 Overview10分钟
Lesson 2 Overview10分钟
2 个练习
Lesson 1 Quiz10分钟
Lesson 2 Quiz8分钟
周
2Module 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 （总计 47 分钟）, 2 个阅读材料, 2 个测验
9 个视频
3.2. Interestingness Measures: Lift and χ25分钟
3.3. Null Invariance Measures5分钟
3.4. Comparison of Null-Invariant Measures7分钟
4.1. Mining Multi-Level Associations4分钟
4.2. Mining Multi-Dimensional Associations2分钟
4.3. Mining Quantitative Associations4分钟
4.4. Mining Negative Correlations6分钟
4.5. Mining Compressed Patterns7分钟
2 个阅读材料
Lesson 3 Overview10分钟
Lesson 4 Overview10分钟
2 个练习
Lesson 3 Quiz10分钟
Lesson 4 Quiz8分钟
周
3Module 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 （总计 56 分钟）, 2 个阅读材料, 2 个测验
10 个视频
5.2. GSP: Apriori-Based Sequential Pattern Mining3分钟
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3分钟
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4分钟
5.5. CloSpan—Mining Closed Sequential Patterns3分钟
6.1. Mining Spatial Associations4分钟
6.2. Mining Spatial Colocation Patterns9分钟
6.3. Mining and Aggregating Patterns over Multiple Trajectories9分钟
6.4. Mining Semantics-Rich Movement Patterns3分钟
6.5. Mining Periodic Movement Patterns7分钟
2 个阅读材料
Lesson 5 Overview10分钟
Lesson 6 Overview10分钟
2 个练习
Lesson 5 Quiz10分钟
Lesson 6 Quiz8分钟
周
4Week 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 （总计 98 分钟）, 2 个阅读材料, 3 个测验
9 个视频
7.2. Previous Phrase Mining Methods10分钟
7.3. ToPMine: Phrase Mining without Training Data12分钟
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14分钟
8.1. Frequent Pattern Mining in Data Streams19分钟
8.2. Pattern Discovery for Software Bug Mining12分钟
8.3. Pattern Discovery for Image Analysis6分钟
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13分钟
8.5. Advanced Topics on Pattern Discovery: Looking Forward4分钟
2 个阅读材料
Lesson 7 Overview10分钟
Lesson 8 Overview10分钟
2 个练习
Lesson 7 Quiz8分钟
Lesson 8 Quiz8分钟
4.3
33 个审阅热门审阅
创建者 GL•Jan 18th 2018
Excellent course. Now I have a big picture about pattern discovery and understand some popular algorithm. Also professor points out the direction for further study.
创建者 DD•Sep 10th 2017
The first several chapters are very impressive. The last three lessons are a little difficult for first-learners. The illustration are clear and easy to understand.
讲师
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
立即开始攻读硕士学位
此 课程 隶属于 University of Illinois at Urbana-Champaign 提供的 100% 在线 Master in Computer Science。如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
关于 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.
关于 Data Mining 专项课程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
