I use kernel k-means algorithm with different kernels and want to see which one is the best. The way i do it is to fix the number of $K$ equal to number of classes (ground truth) and check the accuracy of the clustering result comparing to the true labels.
I also try it 1000 times with different initial point to take the best clustering result. But i think this evaluation is so supervised as i take the best initial point based on my ground truth knowledge.
Isn't it better to split into test/train batches and cluster the test data according to cluster centers obtained from the most accurate clustering on train data?