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:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]> Diese E-Mail enthält vertrauliche und/oder rechtlich geschützte Informationen. Wenn Sie nicht der richtige Adressat sind oder diese E-Mail irrtümlich erhalten haben, informieren Sie bitte sofort den Absender und vernichten Sie diese Mail. Das unerlaubte Kopieren sowie die unbefugte Weitergabe dieser E-Mail ist nicht gestattet. This e-mail may contain confidential and/or privileged infor...{{dropped}}
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