课程信息
4.3
160 个评分
33 个审阅
专项课程

第 4 门课程(共 6 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为16 小时

建议:8 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining
专项课程

第 4 门课程(共 6 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为16 小时

建议:8 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 1 小时

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment....
Reading
1 个视频 (总计 7 分钟), 3 个阅读材料, 1 个测验
Video1 个视频
Reading3 个阅读材料
Syllabus10分钟
About the Discussion Forums10分钟
Social Media10分钟
Quiz1 个练习
Orientation Quiz10分钟
完成时间(小时)
完成时间为 4 小时

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....
Reading
9 个视频 (总计 49 分钟), 2 个阅读材料, 3 个测验
Video9 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
Reading2 个阅读材料
Lesson 1 Overview10分钟
Lesson 2 Overview10分钟
Quiz2 个练习
Lesson 1 Quiz10分钟
Lesson 2 Quiz8分钟
2
完成时间(小时)
完成时间为 1 小时

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....
Reading
9 个视频 (总计 47 分钟), 2 个阅读材料, 2 个测验
Video9 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
Reading2 个阅读材料
Lesson 3 Overview10分钟
Lesson 4 Overview10分钟
Quiz2 个练习
Lesson 3 Quiz10分钟
Lesson 4 Quiz8分钟
3
完成时间(小时)
完成时间为 2 小时

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....
Reading
10 个视频 (总计 56 分钟), 2 个阅读材料, 2 个测验
Video10 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
Reading2 个阅读材料
Lesson 5 Overview10分钟
Lesson 6 Overview10分钟
Quiz2 个练习
Lesson 5 Quiz10分钟
Lesson 6 Quiz8分钟
4
完成时间(小时)
完成时间为 5 小时

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....
Reading
9 个视频 (总计 98 分钟), 2 个阅读材料, 3 个测验
Video9 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
Reading2 个阅读材料
Lesson 7 Overview10分钟
Lesson 8 Overview10分钟
Quiz2 个练习
Lesson 7 Quiz8分钟
Lesson 8 Quiz8分钟
4.3
33 个审阅Chevron Right

热门审阅

创建者 GLJan 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 DDSep 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

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Jiawei Han

Abel Bliss Professor
Department of Computer Science
Graduation Cap

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关于 University of Illinois at Urbana-Champaign

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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....
Data Mining

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