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I was just wondering if there is a way to check if the clustering found by a specific method (e.g., KMeans, DBSCAN, Mixture Models, ...) is significant. Something in line with, there is 5% chance that the clustering found by the algorithm isn't random.

I am struggling to find the correct method for this. Bootstrapping or permutation testing is not an option, since there is no true label.

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  • $\begingroup$ This would first require a precise definition of a cluster, which may be quite hard for some problems. $\endgroup$ – bi_scholar Jan 8 at 14:06
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    $\begingroup$ >Something in line with, there is 5% change that the clustering found by the algorithm isn't random. - this is a common misconception about p-values, they do not guarantee that. I'd recommend you to start with Gaussian Mixture Models and BIC/AIC to address your initial question (even if it is not about pvalues) $\endgroup$ – German Demidov Jan 8 at 14:06
  • $\begingroup$ What do you mean by "there is no true label"? Don't you believe that there is such a thing as a true model (then p-values don't make sense) or are you saying that the problem is that you don't observe the true label? $\endgroup$ – Andreas Dzemski Jan 8 at 14:36
  • $\begingroup$ I have a paper where we suggest a p-value for a clustering (arxiv.org/abs/1801.00332). The method is very model specific though (clustering in panels). $\endgroup$ – Andreas Dzemski Jan 8 at 14:43
  • $\begingroup$ @AndreasDzemski , what I tried to say is that I don't observe the true label. I am not sure if there is a true model. I am trying to express whether the clustering found by the algorithm is not just random noise. $\endgroup$ – Th. Jan 8 at 14:46
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There is the variance ratio criterion, closely related to the F-test.

But if you interpret it as f-test you get absurd high p-values because the test data not being independent to how the data was generated.

You could literally optimize the model to maximize the p-value, and the results will be meaningless, pure overfitting to this one number.

If you want to test your model, it will need to be on fresh data.

A different strategy is used by the gap statistic. IIRC they generate random data assuming a uniform distribution. If you do this 100 times and that data always clusters worse, then you can at least argue that your input data is not uniform... (It could be a diagonal line, or a Gaussian though, neither of which are good clusters...).

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  • $\begingroup$ +1 for gap statistic -- can't get a much more "null" distribution than uniform. $\endgroup$ – Bey Jan 12 at 4:55

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