About this Course
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Approx. 15 hours to complete


Subtitles: English

Skills you will gain

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining

Course 4 of 6 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 15 hours to complete


Subtitles: English

Syllabus - What you will learn from this course

1 hour to complete

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.

1 video (Total 7 min), 3 readings, 1 quiz
1 video
3 readings
About the Discussion Forums10m
Social Media10m
1 practice exercise
Orientation Quiz10m
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.

9 videos (Total 49 min), 2 readings, 3 quizzes
9 videos
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
2 readings
Lesson 1 Overview10m
Lesson 2 Overview10m
2 practice exercises
Lesson 1 Quiz10m
Lesson 2 Quiz8m
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.

9 videos (Total 47 min), 2 readings, 2 quizzes
9 videos
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
2 readings
Lesson 3 Overview10m
Lesson 4 Overview10m
2 practice exercises
Lesson 3 Quiz10m
Lesson 4 Quiz8m
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.

10 videos (Total 56 min), 2 readings, 2 quizzes
10 videos
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
2 readings
Lesson 5 Overview10m
Lesson 6 Overview10m
2 practice exercises
Lesson 5 Quiz10m
Lesson 6 Quiz8m
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.

9 videos (Total 98 min), 2 readings, 3 quizzes
9 videos
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
2 readings
Lesson 7 Overview10m
Lesson 8 Overview10m
2 practice exercises
Lesson 7 Quiz8m
Lesson 8 Quiz8m
42 ReviewsChevron Right

Top reviews from Pattern Discovery in Data Mining

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.

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.



Jiawei Han

Abel Bliss Professor
Department of Computer Science

Start working towards your Master's degree

This course is part of the 100% online Master in Computer 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

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