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

4.4
241 notes
41 avis

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

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.

VB

Nov 07, 2019

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

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26 - 41 sur 41 Examens pour Partitionnement de données

par PABLO P Q

Feb 21, 2019

Nice. Good Course

par Venuu M V R

Apr 11, 2019

The course helped me a lot. I loved this course

par 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.

par Yogesh S M

Jan 27, 2017

Learnt More Here Than I Did At My College!!

par geoffrey a

Sep 02, 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 Kaggle.com 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 aditya p

Feb 15, 2017

good course!

par GANG L

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.

par Anubhav B

Nov 07, 2016

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

par shane

Sep 07, 2017

Very detailed introduction of Clustering techniques.

par 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.

par Alexandre M B

Nov 11, 2017

My analysis is that the assessments do not match the depth of what is explained.

par Aden G

Oct 15, 2016

I am concerned about the last assignment of this course. And I cannot get any help from here.

par Gary C

Jul 24, 2017

For some reason this course felt like it was hurriedly put together. At times the lectures were great, but many times a topic would literally be covered for seconds that would somehow become an involved quiz question. Now I don't mind briefly covering topics, understanding that cluster analysis is a complex topic with many facets. However the quizzes should reflect the lectures. Overall the course felt more like speed dating, when it should be more about the fundamentals of dating.

par Steve S

Jul 18, 2018

I feel like the programming assignments could've been more involved/tied to the clustering algorithms themselves, rather than just submitting a text file with results (e.g., maybe solve a practical problem with an algorithm of choice). Quizzes sometimes contained ambiguous and/or poorly-written questions/answers. Some of the later lectures simply featured equations on a powerpoint and did not involve any examples on how to use them.

par Lei Z

Dec 30, 2016

too theoretical without enough practical quiz and assignment

par Martin L

Dec 14, 2016

Just read the slide., The presentations add very little since the presenter is (stumbling) over just reading the text on the slides.