Calinski - Harabasz criterion for choosing optimal number of clusters












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I've been using the CH criterion to determine the optimal number of clusters for some data I'm working with. I know that in general you would evaluate the criterion for a number of different choices of clusters, and choose the number of clusters which gives the highest CH value, or one where there is a large increase in CH score.



For my data, the optimal number of clusters is 2. Plotting the CH score against number of clusters, I can see the score decreases very quickly if you increase the number of clusters.



My question is this: Since the CH criterion does not evaluate for 1 cluster, how can I be sure using 2 clusters is better than one cluster? I.e how can one determine if there is evidence for any clustering at all in the data?










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    $begingroup$


    I've been using the CH criterion to determine the optimal number of clusters for some data I'm working with. I know that in general you would evaluate the criterion for a number of different choices of clusters, and choose the number of clusters which gives the highest CH value, or one where there is a large increase in CH score.



    For my data, the optimal number of clusters is 2. Plotting the CH score against number of clusters, I can see the score decreases very quickly if you increase the number of clusters.



    My question is this: Since the CH criterion does not evaluate for 1 cluster, how can I be sure using 2 clusters is better than one cluster? I.e how can one determine if there is evidence for any clustering at all in the data?










    share|cite|improve this question









    $endgroup$















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      0





      $begingroup$


      I've been using the CH criterion to determine the optimal number of clusters for some data I'm working with. I know that in general you would evaluate the criterion for a number of different choices of clusters, and choose the number of clusters which gives the highest CH value, or one where there is a large increase in CH score.



      For my data, the optimal number of clusters is 2. Plotting the CH score against number of clusters, I can see the score decreases very quickly if you increase the number of clusters.



      My question is this: Since the CH criterion does not evaluate for 1 cluster, how can I be sure using 2 clusters is better than one cluster? I.e how can one determine if there is evidence for any clustering at all in the data?










      share|cite|improve this question









      $endgroup$




      I've been using the CH criterion to determine the optimal number of clusters for some data I'm working with. I know that in general you would evaluate the criterion for a number of different choices of clusters, and choose the number of clusters which gives the highest CH value, or one where there is a large increase in CH score.



      For my data, the optimal number of clusters is 2. Plotting the CH score against number of clusters, I can see the score decreases very quickly if you increase the number of clusters.



      My question is this: Since the CH criterion does not evaluate for 1 cluster, how can I be sure using 2 clusters is better than one cluster? I.e how can one determine if there is evidence for any clustering at all in the data?







      statistics machine-learning data-analysis clustering






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      asked Dec 21 '18 at 14:33









      darren86darren86

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