When it comes to compare a new clustering algorithm, one always wants to show the advantages of his/her method over existing and well known methods. Going this way may mislead one to ignore disadvantages proposed method.
For clustering results, usually people compare different methods over a set of datasets which readers can see the clusters with their own eyes, and get the differences between different methods results.
There are some metrics, like Homogeneity, Completeness, Adjusted Rand Index, Adjusted Mutual Information, and V-Measure. To compute these metrics, one needs to know the true labels of data-set, so we may test algorithms with classification data-sets to have true labels and then evaluate results.
Another metrics, like Silhouette Coefficient works only with data and clustering results.
I want to know what measures are most preferred and if there is any other metric which does not require true labels of data-set.