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Looking for optimal k in K-Medoids using the Cluster Distance Performance operator

which is supposed to produce the Davies-Bouldin index for k in K-Medoids.

https://docs.rapidminer.com/9.1/studio/operators/validation/performance/segmentation/cluster_distance_performance.html

Used the Tutorial to try to duplicate the process. My results in sequence are:

1 Process looking for k produced by the Davies-Bouldin method.

Result Folder view

Result Graph

Image: https://us.v-cdn.net/6030995/uploads/editor/jp/jjsbmur9evpp.png

Result Centroid table

Image: https://us.v-cdn.net/6030995/uploads/editor/q9/quu8vsewnm8s.png

Result Annotations

Image: https://us.v-cdn.net/6030995/uploads/editor/ii/gzre4woam1xh.png

AutoModel Results k-means-summary. K-medoids is not a model choice.

Image: https://us.v-cdn.net/6030995/uploads/editor/dx/h8fzr4e8i6h0.png

What am I missing here looking for the Davies-Bouldin method?

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Accepted answers

Caperez

Hi @tonyboy9,

I understand that you miss the Davies-Bouldin Index in Automodel clustering models. You can apply the Cluster Distance Performance Operator to the resulting model from Automodel. I hope that can help you.

Best

tonyboy9

Many thanks to ceaperez for suggesting I try the Cluster Distance Performance Operator.
I used the steps in the tutorial.

Image: https://us.v-cdn.net/6030995/uploads/editor/rg/5utmyy41it8t.png

This was my first clue Davies-Boudin could be found.

Image: https://us.v-cdn.net/6030995/uploads/editor/6c/ycdpxov8q53a.png

I used the tutorial to set up the process in RapidMiner Studio.

Image: https://us.v-cdn.net/6030995/uploads/editor/20/yhl75remu78q.png

After I ran the above process, on Cluster Model I clicked on PerformanceVector.

Image: https://us.v-cdn.net/6030995/uploads/editor/c1/vktbz5knex2p.png

On this result, Davies-Bouldin is the choice in the left bar.

Image: https://us.v-cdn.net/6030995/uploads/editor/d0/ovknmdoi7z7h.png

And here you have it, Davies-Bouldin. It's only a start, now that I understand how to play with the k parameter inside the operator.

Image: https://us.v-cdn.net/6030995/uploads/editor/ge/69caogo30480.png

All comments

Caperez

Hi @tonyboy9,

The K-means is a very useful and common used technique.

Both K-Means and K-medoids are partitional methods but the K-means is centered into minimize the total square error and the K-medoids is centered into minimizes the sum of dissimilarities between points.

Best

Marco_Barradas

Hi @tonyboy9,

Maybe this thread could help you understand and find that optimal number.

https://community.rapidminer.com/discussion/comment/61654#Comment_61654

Caperez

tonyboy9

Many thanks to ceaperez for suggesting I try the Cluster Distance Performance Operator.
I used the steps in the tutorial.

This was my first clue Davies-Boudin could be found.

I used the tutorial to set up the process in RapidMiner Studio.

After I ran the above process, on Cluster Model I clicked on PerformanceVector.

On this result, Davies-Bouldin is the choice in the left bar.

And here you have it, Davies-Bouldin. It's only a start, now that I understand how to play with the k parameter inside the operator.