I want to cluster a dataset without prior knowledge on the correct amount of clusters. For different algorithms (i.e. k-means, gmm...) I can iterate through different values and try to find the best solution for any given algorithm (i.e. ellbow-curve, silhouette-coefficient etc.).
But I get very different results - as expected with different algorithms. K-Means is good for spherical clusters, density-based approaches for totally different cluster shapes.
Now the actual question: How do I select the "best" unsupervised machine learning algorithm to cluster my specific dataset? Is there a scientific way to go? Any comparative metric (like rand index) that can be used? Some papers on that topic? Maybe even a flowchart?