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Learner Reviews & Feedback for Cluster Analysis in Data Mining by University of Illinois at Urbana-Champaign

399 ratings

About the Course

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....

Top reviews


Dec 17, 2018

This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.


Sep 18, 2017

Very informative lectures, wonderful assignments. This course isn't so easy but it gives you real knowledge and useful experience.

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26 - 50 of 62 Reviews for Cluster Analysis in Data Mining

By Dr. P N

Oct 14, 2020

A wonderful learning experience !

By Pavan G

Oct 2, 2017

Explained with nice examples

By Leela P

Jan 16, 2017

Very useful and well taught


May 1, 2017

Clustering demytified

By Ankit

Feb 12, 2020

Fantastic course

By Christopher D

Nov 8, 2016

Great course!


Mar 17, 2019



Apr 9, 2019


By Oren Z B M

Jun 7, 2017

Very good

By Hernán C V

Jul 1, 2017


By vaseem a

Apr 8, 2019


By Alan J R

Feb 20, 2020


By Valerie P

Jul 11, 2017


By geoffrey a

Sep 2, 2017

Good, thorough coverage -- for a 4-week course -- of how to cluster. I liked the evaluation of clustering topic especially. Very few other instructors seem to discuss the vitally important evaluation of clustering results in any depth when they teach clustering. Dr. Han explained a comprehensive framework for understanding the effectiveness of any clustering system. I had never seen some of this material before, even though clustering was a topic appearing in a couple of other data science or machine learning courses that I have taken in the past. Ideally I would even wish to see this course extended to 6 or 8 weeks, so that case studies on difficult real datasets can be clustered. For example I had a terribly difficult ordeal last year before I took this course, trying to cluster the dataset of the BOSCH competition. It has about 90% missing data in every row, and there are 2 million rows in total, and about 4500 columns! Kaggle's BOSCH is a SUPER tough dataset to work with! I hope to come back to try the BOSCH dataset again using my new knowledge of clustering some time soon. The reason I chose to run unsupervised clustering on this BOSCH dataset, which is ostensibly intended for supervised learning, is to eliminate significant amounts of the missing data from being exposed to multiple individual supervised learning models by prior clever grouping of examples. I am still postulating to the current day that clustering and creating another unique supervised learning model for each cluster is the most important step to eliminating missing data in this particular problem.

By David M

Jun 12, 2020

Enjoyed the course. Though there is no programming content, the assignments require such. So, participants should have some prerequisite skills in either R, Phyton or other statistical software to perform. What I like is that the contents cover the "maths" of cluster analysis, though not very deep.

By Cassius d O P

Apr 17, 2021

It was definitely an instructive course. I liked a lot the insights and discussion about different clustering methods and algorithms. The downside of this course is the scanty discussion about the practical implementation/usage of these algorithms.


Jan 26, 2018

This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.

By Devender B

Mar 10, 2019

Useful theory. It will be challenging for non-math students. and also lecturer's native language influence iis going to be challening as well to follow along.

By Umesh G

Apr 28, 2019

Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.

By Haf M

Jul 22, 2021

The course is good. learned alot but videos are boring and hard to understand due to more and more text on slides

By Alexander S

Dec 16, 2019

Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.

By Anubhav B

Nov 7, 2016

The course is very insightful and very helpful for the data mining studies at university courses.

By Ridowati G

Jan 24, 2021

The material is too general, does not provide examples. So it's difficult when doing the exam.


Jul 28, 2020

Covers great deal of topics and various aspects of clustering

By shane

Sep 7, 2017

Very detailed introduction of Clustering techniques.