Currently, I am doing a clustering for two sets of data. One smaller dataset (about 100 data) got ground truth labels, and one larger dataset (about 2000 data) has no ground truth labels.
For the smaller dataset, obviously, I can obtain quantitative results like accuracy, sensitivity and specificity.
However, for the larger dataset, I have no ground truth and couldn't get any useful quantitative results.
The only thing I found useful is the 'mean silhouette value', which can measure the cluster performance. However, it based on some distance measure that can only tell people how separate are the clusters. I am wondering if there are other 'better' or 'more appropriate' quantitative analysis for data without labels.
Because the data are without labels, I am also wondering if we can somehow have a 'uncertainty' measure about the clustering results like how confident about the cluster results?
For the smaller dataset with labels, except accuracy, sensitivity and specificity, any other quantitative results I can get? For the classification algorithm, we can do a cross-validation, is there any method we can use to do such a cross-validation for clustering? Also, can we get ROC analysis for clustering task?