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

4.4
234 ratings
40 reviews

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

ES

Dec 18, 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.

DD

Sep 25, 2017

A very good course, it gives me a general idea of how clustering algorithm work.

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1 - 25 of 40 Reviews for Cluster Analysis in Data Mining

By zshowing

Dec 25, 2017

The instructor basically reads the slides line by line, with very few examples.

By NACHO

Apr 16, 2017

Redundant, poor explanations and a complete lack of examples about the general concepts and the foundations of this discipline. The interaction between the teacher and the slides is limited to a reading exercise that does not provide any add value at all. Very dissapointed and still wondering if this course is worth my attention -and extremely limited time- or not. Plenty of room for improvement.

By Daniel B

Feb 21, 2017

I have sat through 4 of the lessons and I am not very impressed. I fell that the topic is very interesting, but the professor does not do a very good job explaining the algorithms. It may be because I do no have the textbook, but overall a rather poor course. There need to be a little more explanation beyond the slides.

By Bernd

Oct 27, 2017

Great course that provides a good overview of different clustering approaches and how to deploy them to various problems. I found the lecture material unclear or vague at times, so that for certain topics understanding heavily depends on one diving through the provided reading material (which I found very helpful). However, the topic of evaluation is very dense in the lectures and the provided book chapters do not provide relevant insights as well, making the programming assignment for this part quite challenging (at least if not already deeply familiar already with the concepts involved). Be ready to invest effort to make the most of this.

By ADARSHPANDEY

Dec 24, 2017

Course is very good I learnt about a lot of things related to clustering. Actually it is a very good introductory course in clustering compared to the resources available online in general. Although few things that I think might help improve the course

i) Course only implements K-Means which is a very simple algorithm, instead of this or in addition to this implementation of few advanced algorithms like DBSCAN or CHAMELEON should be added.

ii) A no. of times prof only seems to be reading the slides which make things a little bit unclear i.e, the sentences used should be more common or explanatory rather than just reading the slides which the student itself can.

Apart from these things I truly enjoyed and learned many new things.

Thank you everyone involved in developing this course

By Eric A S

Dec 18, 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.

By vaseem a

Apr 09, 2019

awesome

By KRUPAL J K

Apr 09, 2019

VERY GOOD

By VIDUSHI M

Mar 17, 2019

Excellent!

By Srinath R M

Jul 10, 2018

Gave a very good understanding of cluster analysis - explaining all different methods and algorithms, the benefits and drawbacks of each. The tool ClusterEng looks very good and can help in a lot of situations. Thank so much

By barbara

Aug 01, 2018

This course is a great resource to learn about the different clustering algorithms out there. I need to solve a clustering problem in my research and my knowledge about clustering ended at kmeans. The course teaches systematic ways to find out whether you should be clustering your data in the first place, what clustering algorithm should be best for your data, and how to evaluate the goodness of the algorithm and the used parameters. Many unknown unknowns have been illuminated to me by the course.

By Ian W

Aug 20, 2018

Nice lecture.

The programming assignment is difficult, more instructions could be provided.

By Valerie P

Jul 11, 2017

E

By Oren Z

Jun 07, 2017

Very good

By Vasco D S N C D

Nov 22, 2017

Excellent overview of many clustering algorithms!

By Tanan K

Oct 10, 2017

Very intense and required complex thinking and programming skill

By Pavan G

Oct 02, 2017

Explained with nice examples

By Glushko O V

Sep 19, 2017

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

By Hernan C V

Jul 01, 2017

Awesome!

By Jose A E H

Jul 12, 2017

This course along with the Reading material proposed will give you a big picture of how clustering algorithms work, as well as clustering validation methodologies. It is really useful if you are thinking about applying such algorithms and understanding the state-of-the-art.

By Leela P

Jan 16, 2017

Very useful and well taught

By AJETUNMOBI O

May 01, 2017

Clustering demytified

By Darren

Sep 25, 2017

A very good course, it gives me a general idea of how clustering algorithm work.

By Christopher D

Nov 08, 2016

Great course!

By PABLO P Q

Feb 21, 2019

Nice. Good Course