For the dissimilarity metric I would suggest manhattan, as provided by dist
(base package), daisy, agnes (both cluster package), for in your case a common
"0" is meaningful - means that both pysicians didn't see the
patient.
When using complete linkage you can see exactly how many patients (seen or not
seen) the pysicians in one cluster have at least in common. If the height goes
up too fast so that you would have to extract to many clusters you can use
average linkage.
For the clustering you can use hclust from the base package, agnes from the
cluster package, or, when hclust or agnes run out of memory, clara (see thread
[R] cluster analysis for 80000 observations)
sincerely, Markus
___________________
Markus Preisetanz
Consultant
Client Vela GmbH
Albert-Roßhaupter-Str. 32
81369 München
fon: +49 (0) 89 742 17-113
fax: +49 (0) 89 742 17-150
mailto:markus.preisetanz@clientvela.com
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