I would like to use the calinski-Harabasz Index to evaluate different runs of the DBSCAN algorithm (different min_points).
According to sklearn's documentation, the index is "generally higher for convex clusters than other concepts of clusters, such as density based clusters like those obtained through DBSCAN.".
Since I am comparing different runs of DBSCAN i.e not comparing DBSCAN to, say, K-Means, would it make sense to use the index?
Please note that I have tried to use metrics specifically designed for Desnsity-Based clusters like the Density-Based Clustering Validation DBCV, but its computational complexity was bigger than what I can afford (I am clustering around 200,000 real-valued vectors of dimension 300). The main reason for choosing the calinski-Harabasz Index is that it is fast to compute.