How to analyse the integrity of clustering with no ground truth labels?
Don't just rely on some heuristic, that someone proposed for a very different problem.
Key to clustering is to carefully consider the problem that you are working on. What is the proper way of proposing the data? How to scale (or not scale)? How to measure the similarity of two records in a way that it quantifies something meaningful for your domain.
It is not about choosing the right algorithm; your task is to do the math that relates your domain problem to what the algorithm does. Don't treat it as a black box. Choosing the approach based on the evaluation step does not work: it is already too late; you probably did some bad decisions already in the preprocessing, used the wrong distance, scaling, and other parameters.
I'm clustering data (trying out multiple algorithms) and trying to evaluate the coherence/integrity of the resulting clusters from each algorithm. I do not have any ground truth labels, which rules out quite a few metrics for analysing the performance. 
So far, I've been using Silhouette score as well as calinski harabaz score (from sklearn). With these scores, however, I can only compare the integrity of the clustering if my labels produced from an algorithm propose there to be at minimum, 2 clusters - but some of my algorithms propose that one cluster is the most reliable.
Thus, if you don't have any ground truth labels, how do you assess whether the proposed clustering by an algorithm is better than if all of the data was assigned in just one cluster?