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I have 114 vectors with 6 boolean attributes. I saw that might be several distinct clusters in a simple visualization. K-means clustering on the transformed vectors (true = 1, false = 0) results in roughly the clusters that I had seen in the visualization.

However, I am not sure what the most appropriate clustering method for this kind of data is, and how to determine the confidence in those factors (the k-means results change every time due to randomization). Should I treat the data as nominal or as numerical data?

What would be the best way to do a cluster analysis on this kind of data in R?

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  • $\begingroup$ You could also try one of the hierarchical methods with an appropriate measure of similarity/dissimilarity. For tha latter, Sokal-Michener, Sokal-Sneath, Rogers-Tinimoto, Russell-Raova, Jaccard, Czekanowski, Sokal-Sneath #2 measures of similarity comes to mind. $\endgroup$ – Roman Luštrik Apr 29 '12 at 6:36
  • $\begingroup$ stats.stackexchange.com/questions/6252/… $\endgroup$ – Etienne Low-Décarie Apr 30 '12 at 1:04
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I would have a glance to the mona function in the cluster package. It seems to address your question.

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