About this Course

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Learner Career Outcomes

33%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 14 hours to complete
English
Subtitles: English, Korean

Skills you will gain

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining

Learner Career Outcomes

33%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 14 hours to complete
English
Subtitles: English, Korean

Offered by

University of Illinois at Urbana-Champaign logo

University of Illinois at Urbana-Champaign

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.

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Course Orientation

1 hour to complete
1 video (Total 7 min), 3 readings, 1 quiz
1 video
3 readings
Syllabus10m
About the Discussion Forums10m
Social Media10m
1 practice exercise
Orientation Quiz10m
4 hours to complete

Module 1

4 hours to complete
9 videos (Total 49 min), 2 readings, 3 quizzes
9 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
2 readings
Lesson 1 Overview10m
Lesson 2 Overview10m
2 practice exercises
Lesson 1 Quiz10m
Lesson 2 Quiz8m
Week
2

Week 2

1 hour to complete

Module 2

1 hour to complete
9 videos (Total 47 min), 2 readings, 2 quizzes
9 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
2 readings
Lesson 3 Overview10m
Lesson 4 Overview10m
2 practice exercises
Lesson 3 Quiz10m
Lesson 4 Quiz8m
Week
3

Week 3

2 hours to complete

Module 3

2 hours to complete
10 videos (Total 56 min), 2 readings, 2 quizzes
10 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
2 readings
Lesson 5 Overview10m
Lesson 6 Overview10m
2 practice exercises
Lesson 5 Quiz10m
Lesson 6 Quiz8m
Week
4

Week 4

5 hours to complete

Week 4

5 hours to complete
9 videos (Total 98 min), 2 readings, 3 quizzes
9 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
2 readings
Lesson 7 Overview10m
Lesson 8 Overview10m
2 practice exercises
Lesson 7 Quiz8m
Lesson 8 Quiz8m

Reviews

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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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