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.

• Your question is broad, you are not asking anything specific. There are many methods of clustering, many distant measures, many ways to validate clusters. You certainly have to read more about cluster analysis before you feel youself able to make conscious choices among those lots of options. Oct 28, 2013 at 17:43
• I agree with ttnphns. If you have so much data, clustering will be messy no matter what algorithm you use. Perhaps try to reduce the dimensions of your dataset (multiple correspondance analysis) Oct 28, 2013 at 18:58
• Ok. Will let's assume my data set was small enough in terms of # of columns & rows. Say something just a tad larger than the example I included. And say for example I used three or so clustering algorithms and saved the results of each. Obviously each algorithm (and various methods to assess "similiarity") might yield a different set of clusters. But I have seen that some objects tend to be clustered identically or at least similarly by each algorithm. Oct 29, 2013 at 18:23
• I'm thinking something along the lines of randomForest in terms of the concept of voting. In my example objects G & Z were clustered together by all three algorthms so I would be confident in using this as valid cluster in further analysis. Objects S & F also clustered together. I might then say S&F&R were clustered together by two algorithms so I will decide that that is a valid cluster. My question really is if there something that helps facilitate this process I'm proposing. If you think I am still not being clear but have a suggestion on how I could be clearer please let me know. Oct 29, 2013 at 18:36