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Cluster Analysis in Data Mining に戻る

Cluster Analysis in Data Mining, イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign)

4.4
216件の評価
40件のレビュー

このコースについて

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....

人気のレビュー

by 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.

by DD

Sep 25, 2017

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

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40件のレビュー

by Umesh Giri

Apr 28, 2019

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

by Venuu

Apr 11, 2019

The course helped me a lot. I loved this course

by KRUPAL J. KATHROTIA

Apr 09, 2019

VERY GOOD

by vaseem akram

Apr 09, 2019

awesome

by VIDUSHI MOHAN

Mar 17, 2019

Excellent!

by 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.

by PABLO PEREZ QUINECHE

Feb 21, 2019

Nice. Good Course

by 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.

by Ian Wang

Aug 20, 2018

Nice lecture.

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

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.