About this Course
4.3
156 ratings
32 reviews
Specialization

Course 4 of 6 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 16 hours to complete

Suggested: 8 hours/week...
Available languages

English

Subtitles: English...

Skills you will gain

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining
Specialization

Course 4 of 6 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 16 hours to complete

Suggested: 8 hours/week...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
1 hour to complete

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment....
Reading
1 video (Total 7 min), 3 readings, 1 quiz
Video1 video
Reading3 readings
Syllabus10m
About the Discussion Forums10m
Social Media10m
Quiz1 practice exercise
Orientation Quiz10m
Hours to complete
4 hours to complete

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 videos (Total 49 min), 2 readings, 3 quizzes
Video9 videos
1.2. Frequent Patterns and Association Rules5m
1.3. Compressed Representation: Closed Patterns and Max-Patterns7m
2.1. The Downward Closure Property of Frequent Patterns3m
2.2. The Apriori Algorithm6m
2.3. Extensions or Improvements of Apriori7m
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3m
2.5. FPGrowth: A Pattern Growth Approach8m
2.6. Mining Closed Patterns3m
Reading2 readings
Lesson 1 Overview10m
Lesson 2 Overview10m
Quiz2 practice exercises
Lesson 1 Quiz10m
Lesson 2 Quiz8m
Week
2
Hours to complete
1 hour to complete

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 videos (Total 47 min), 2 readings, 2 quizzes
Video9 videos
3.2. Interestingness Measures: Lift and χ25m
3.3. Null Invariance Measures5m
3.4. Comparison of Null-Invariant Measures7m
4.1. Mining Multi-Level Associations4m
4.2. Mining Multi-Dimensional Associations2m
4.3. Mining Quantitative Associations4m
4.4. Mining Negative Correlations6m
4.5. Mining Compressed Patterns7m
Reading2 readings
Lesson 3 Overview10m
Lesson 4 Overview10m
Quiz2 practice exercises
Lesson 3 Quiz10m
Lesson 4 Quiz8m
Week
3
Hours to complete
2 hours to complete

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 videos (Total 56 min), 2 readings, 2 quizzes
Video10 videos
5.2. GSP: Apriori-Based Sequential Pattern Mining3m
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3m
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4m
5.5. CloSpan—Mining Closed Sequential Patterns3m
6.1. Mining Spatial Associations4m
6.2. Mining Spatial Colocation Patterns9m
6.3. Mining and Aggregating Patterns over Multiple Trajectories9m
6.4. Mining Semantics-Rich Movement Patterns3m
6.5. Mining Periodic Movement Patterns7m
Reading2 readings
Lesson 5 Overview10m
Lesson 6 Overview10m
Quiz2 practice exercises
Lesson 5 Quiz10m
Lesson 6 Quiz8m
Week
4
Hours to complete
5 hours to complete

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 videos (Total 98 min), 2 readings, 3 quizzes
Video9 videos
7.2. Previous Phrase Mining Methods10m
7.3. ToPMine: Phrase Mining without Training Data12m
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14m
8.1. Frequent Pattern Mining in Data Streams19m
8.2. Pattern Discovery for Software Bug Mining12m
8.3. Pattern Discovery for Image Analysis6m
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13m
8.5. Advanced Topics on Pattern Discovery: Looking Forward4m
Reading2 readings
Lesson 7 Overview10m
Lesson 8 Overview10m
Quiz2 practice exercises
Lesson 7 Quiz8m
Lesson 8 Quiz8m
4.3
32 ReviewsChevron Right

Top Reviews

By 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.

By 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.

Instructor

Avatar

Jiawei Han

Abel Bliss Professor
Department of Computer Science
Graduation Cap

Start working towards your Master's degree

This course is part of the 100% online Master of Computer Science in Data Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

About 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. ...

About the Data Mining Specialization

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.