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

tonyboy9tonyboy9 Member Posts: 93 Contributor II
which is supposed to produce the Davies-Bouldin index for k in K-Medoids.

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

Result Centroid table

Result Annotations

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

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

Best Answers

  • ceaperezceaperez Member Posts: 218   Unicorn
    Solution Accepted
    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.

  • tonyboy9tonyboy9 Member Posts: 93 Contributor II
    Solution Accepted

    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.


  • ceaperezceaperez Member Posts: 218   Unicorn
    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.

  • MarcoBarradasMarcoBarradas Administrator, Employee, RapidMiner Certified Analyst, Member Posts: 225   Unicorn
    Hi @tonyboy9,

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