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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 394 ratings

Start your review of Cluster Analysis in Data Mining

  • Gregory J Hamel ( Life Is Study) 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...
  • Bijaya Zenchenko

    Bijaya Zenchenko 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.
  • Kristina Šekrst 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.
  • César Alba

    César Alba completed this course.

  • Colin Khein completed this course.

  • Mark Henry Butler completed this course.

  • Stephane Mysona

    Stephane Mysona completed this course.

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