Types of Clustering Methods: Overview and Quick Start R Code

kassambara | 23/09/2017 | 80126 | Comments (12) | Cluster Analysis in R: Practical Guide

Last update : 16/01/2018

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Comments

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Comment

kassambara

Administrator

#407 03/29/2018 at 20h49

The following articles might help for categorical variables:

Multiple Correspondence Analysis

HCPC - Hierarchical Clustering on Principal Components

Comment

Visitor

#404 03/28/2018 at 17h09

nice work, congratulations!
but.. what happens if I we have only binary variables in my dataset? I think at least the metric has to be different...

Comment

Visitor

#388 03/18/2018 at 14h39

Thanks a lot. You help me to understand statistics

Comment

Jorge

Visitor

#387 03/16/2018 at 22h28

Very nice work!! Congratulations!!!

Comment

kassambara

Administrator

#362 01/27/2018 at 09h45

Your positive feedback is highly appreciated

Comment

Visitor

#361 01/25/2018 at 21h17

Very helpful. Cant thank you enough.

Comment

kassambara

Administrator

#357 01/16/2018 at 20h16

Please make sure you have the latest version of factoextra.

I already modified this issue in the package but I forgot to update the article.

The article is now updated. Thank you for pointing this out.

Comment

Visitor

#354 01/16/2018 at 15h38

Hello,

Thank you very much for sharing your work. I just have a small issue with the Hopkins statistic. In the literature, it says that the data is highly clusterable when the value of the Hopkins statistic is closer to 1 and that 0.5 corresponds to the uniform distribution. Is this statistic implanted differently in the factoExtra library? since here it's written that for highly clusterable data we shall expect values close to 0.

Thanks in advance

Comment

kassambara

Administrator

#347 01/12/2018 at 22h57

Thank you for your feedback, highly appreciated!!

Comment

Yong Kai

Member

#344 01/12/2018 at 02h40

Thanks for your amazing works! It is simple, intuitive and hands-on. I'd say this is an ideal starter article for beginners or a refresher course for experienced practitioners on the applied clustering techniques with R.

Comment

Visitor

#254 10/05/2017 at 16h09

Wonderful work! It will be helpful if you note down the code as well which map cluster number against each state.

Comment

Gilloux

Visitor

#221 09/29/2017 at 10h07

Thanks a lot for this very interesting recap of clustering methods.
This is what I was looking for ... as when it comes to non supervised methods, we have to run numerous solutions and try to guess if the result is fair/acceptable.

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Articles - Cluster Analysis in R: Practical Guide

Types of Clustering Methods: Overview and Quick Start R Code