# Cluster interpretation

I'm running flat clustering algorithm on my dataset that contains numeric (not categorical) data.

Is there a method that can give me interpretation of clusters, and emphasize what are the most important variables and their range of values in cluster?

Something like - This cluster contains mostly elements for which Property XY has small values, and Property YZ has great values?

Or do I need post processing, like count elements in cluster that have value above certain threshold and something similar?

Thanks

• The question is really not clear. Presumably, by "This parameter" you mean 'this cluster'. What could "Parameter XY" etc mean? Do you mean, 'the mean vector'? What do you mean by "the most important parameters"? How can 1 parameter be more important than another? Etc. – gung Oct 13 '14 at 15:57
• @gung I guess "parameter" here from eugen means "variable" in more usual statistical parlance. People in several sciences use "parameter" as an all-purpose synonym for property or variable; not only does that seem odd to people saturated in statistics, it contradicts the meaning at least some of us learned in elementary mathematics, but there you go. – Nick Cox Oct 13 '14 at 17:18
• @Nick Cox thank you for clarifying that. Yes, I have meant variable when I said parameter. – Marko Oct 13 '14 at 18:55

Once you've added the cluster identifier to each data point, you can use that as a target (the dependent variable) for in a decision tree. If you're using R, I found a good blog posting that should give you a start with several R packages: tree, rpart, party, partykit, maptree, evtree, etc. Harder to interpret but more flexible would be MARS (implemented in R's earth package).