How to evaluate/validate clusters using multiple clustering methods 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.
 A: As daniellopez46 noted, I think you are thinking of consensus clustering where you basically form an ensemble of different clustering runs. What is a bit strange here is that you would want the ensemble to contain results from different clustering methods which can be very misleading. I say this because unlike supervised learning, unsupervised learning always has in a larger or smaller degree a subjective component as you need to have an idea of what you consider a grouping you would be interested in based on your data. Elaborating a bit, clustering is labeling observations based on what relationship they have with other observations in your feature space. Different clustering algorithms will understand this in a totally different fashion as they are looking for different things. Depending on what kind of topology you are looking for you will (as a human) be satisfied with what one clustering algorithm produced on some data set and be totally dissatisfied with what it did on another data set. Look at this question I recently answered, where you can see a diagram of how different clustering techniques treat the same data sets.
Another thing that should be noted is that consensus clustering is still very new and is basically just being explored so don't take it as panacea.
