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University of Illinois at Urbana-Champaign

Cluster Analysis in Data Mining

University of Illinois at Urbana-Champaign via Coursera


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.


  • 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


2.6 rating, based on 7 Class Central reviews

4.5 rating at Coursera based on 403 ratings

Start your review of Cluster Analysis in Data Mining

  • 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 gr…
  • Bijaya Zenchenko
    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.
  • 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.
  • César Alba
  • Stephane Mysona

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