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Would be interesting to see PCA and CA* applied to the same data.

On a quick google trip I found

[1] Principal Component Analysis of Senate Voting Patterns (http://escholarship.org/uc/item/3xm9z62w#page-1)

[2] Shlomo S. Sawilowsky, Real data analysis; Chapter 28, Principal Component Analysis of Senate Voting Patterns (on Google Books)

* Principal Component and Correspondence Analyses respectively -- http://en.wikipedia.org/wiki/Principal_component_analysis and http://en.wikipedia.org/wiki/Correspondence_analysis

I'm not sure this would give the same kind of results, it was shown [1] that Kleinberg's HITS algorithm is PCA, and HITS gives [2] hubs (nodes linked to many others) and authorities (nodes linked from many hubs). I have the intuition that this would qualify the floor leaders as authorities, but I'd have to run the numbers.

Anyway, in order to obtain groups from PCA (that is, groups which are not only "visual"), one would have to run some kind of clustering algorithm on the reduced data.

[1] http://www.uclouvain.be/cps/ucl/doc/iag/documents/WP125_Saer...

[2] http://en.wikipedia.org/wiki/HITS_algorithm

It's not the same data, but I did a PCA with the voting record of Canadian MPs. It's a bit old, so it's in flash instead of html5. You can access it here: http://www.votum.ca

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