From some reading I did online, I understand that there are various methods for determining "similarity" used by different clustering algorithms. I am curious if it is good practice to run multiple clustering algorithms/methods (i.e Hiearchical w/ Ward, single linkage, centroid, etc or maybe even K-means) on a dataset and if there is some automated way to to get a "consensus" of clusters. In other words to get some sense of confidence that the right items are clustered together. Items that tend to cluster together using various methods would be considered valid. For example in my example below G and Z tend to cluster together using multiple methods as do S and F.
Label = what I am clustering; X & Y are my variables I use to cluster; Cluster1-3 are the results of three clustering algorithms.
Edit: I removed a side note I had here regarding how large the actual data set I plan to use might be so as not to detract from the main questions.