Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Online Course

Cluster Analysis in Data Mining

University of Illinois at Urbana-Champaign via Coursera

294
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Effort 4-6 hours a week
  • Duration 4 weeks long
  • Learn more about MOOCs

Taken this course? Share your experience with other students. Write review

Overview

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Syllabus

Course Orientation
-You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Module 1

Week 2

Week 3

Week 4

Course Conclusion
-In the course conclusion, feel free to share any thoughts you have on this course experience.

Taught by

Jiawei Han

Tags

Help Center

Most commonly asked questions about Coursera

Reviews for Coursera's Cluster Analysis in Data Mining Based on 7 reviews

  • 5 star 0%
  • 4 stars 29%
  • 3 star 14%
  • 2 stars 43%
  • 1 star 14%

Did you take this course? Share your experience with other students.

Write a review
  • 1
Gregory S
4 years ago
by Gregory completed this course and found the course difficulty to be medium.
Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign. The course is a 4-week overview of data clustering: unsupervised learning methods that attempt to group data into clusters of related or similar observations. The course covers two most common clustering methods--K means and hierarchical clustering--as well as more than a dozen other clustering algorithms. Grading is based on 4 weekly quizzes with 3 attempts each.

Cluster Analysis is taught by Professor Jiawei Han who was the instruct…
2 people found
this review helpful
Was this review helpful to you? Yes
Bijaya Z
4 years ago
Bijaya completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
I thought the class was good for someone who already knows how to apply clustering analysis to data. I have been using different clustering algorithms in the past, this class gave me a greater overview of the other clustering methods that existed that I hadn't been exposed to. I do not recommend this for someone who is new to the concept/application of clustering.
1 person found
this review helpful
Was this review helpful to you? Yes
Kristina Š
7 months ago
by Kristina completed this course and found the course difficulty to be medium.
I liked the way I was able to learn more about the newest trends in clustering algorithms, but there was too much theory, and too little practice. However, it was a fun experience, but I hope in the second iteration that the ratio of the programming assignments and the theoretical descriptions of various algorithms and papers will be equal.

Was this review helpful to you? Yes
Mark B
4 years ago
by Mark completed this course.
Was this review helpful to you? Yes
Stephane M
3 years ago
Stephane completed this course.
Was this review helpful to you? Yes
Colin K
4 years ago
by Colin completed this course.
Was this review helpful to you? Yes
César A
4 years ago
César completed this course.
Was this review helpful to you? Yes
  • 1

Class Central

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free

Never stop learning Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free