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Partitionnement de données , Université de l'Illinois à Urbana-Champaign

4.4
213 notes
39 avis

À propos de ce 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

par 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.

par DD

Sep 25, 2017

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

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39 avis

par Venuu

Apr 11, 2019

The course helped me a lot. I loved this course

par KRUPAL J. KATHROTIA

Apr 09, 2019

VERY GOOD

par vaseem akram

Apr 09, 2019

awesome

par VIDUSHI MOHAN

Mar 17, 2019

Excellent!

par Devender Bejju

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 PABLO PEREZ QUINECHE

Feb 21, 2019

Nice. Good Course

par Eric Antoine Scuccimarra

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.

par Ian Wang

Aug 20, 2018

Nice lecture.

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

par 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.

par Steve Sekowski

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.