Given m, p and t non-zero natural numbers:
m is the number of clustering methods,
p is the number of internal measures for cluster validation (i.e halkidi, sd, calinski_harabaz, davies_bouldin...),
t is the number of different datasets.
I run the m clustering methods on t datasets and I measure the clustering results of each method with each measure from the p internal measures. So each internal measure has m*t values.
I don't see how to evaluate the performance of the methods in order to choose the best method clustering. Because a clustering method is not the best for each measure.
Is there a way or a technique to identify the best clustering method.
I note that the method clustering are related to different versions of k-means that doesn't produce the same number of cluster and also not the same partitions.