unfortunately I have not been able to find an answer to my question and have not found a solution here in the forum. My problem:

I am performing a cluster analysis of binary scaled data (yes/no). For this I use the Jaccard coefficient, which measures the similarity between objects. I also use the average linkage algorithm.

How can I determine the optimal number of clusters? Unfortunately, I have not found a suitable stopping rule yet. Calinski/Harabasz for example is only suitable for metric data. I was also recommended the criterion according to Mojena, but due to the use of a similarity measure, the fusion level decreases continuously, which is why the fusion value to be determined according to Mojena also decreases continuously.

I am grateful for any hint how I can determine computationally an optimal cluster number!

Many greetings from Aachen (Germany)!