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Avis et commentaires pour d'étudiants pour Partitionnement de données par Université de l'Illinois à Urbana-Champaign

393 évaluations

À propos du cours

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....

Meilleurs avis


17 déc. 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.


6 nov. 2019

Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks

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26 - 50 sur 62 Avis pour Partitionnement de données

par Dr. P N

14 oct. 2020

A wonderful learning experience !

par Pavan G

2 oct. 2017

Explained with nice examples

par Leela P

16 janv. 2017

Very useful and well taught


1 mai 2017

Clustering demytified

par Ankit

12 févr. 2020

Fantastic course

par Christopher D

8 nov. 2016

Great course!


17 mars 2019



9 avr. 2019


par Oren

7 juin 2017

Very good

par Hernan C V

1 juil. 2017


par vaseem a

8 avr. 2019


par Alan J R

20 févr. 2020


par Valerie P

11 juil. 2017


par geoffrey a

2 sept. 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.

par David M

12 juin 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.

par Cassius d O P

17 avr. 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.

par GANG L

26 janv. 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.

par Devender B

10 mars 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.

par Umesh G

28 avr. 2019

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

par Haf M

22 juil. 2021

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

par Alexander S

16 déc. 2019

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

par Anubhav B

7 nov. 2016

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

par Ridowati G

24 janv. 2021

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


28 juil. 2020

Covers great deal of topics and various aspects of clustering

par shane

7 sept. 2017

Very detailed introduction of Clustering techniques.