Since clustering methods begin with pairwise distances among observations,
why not measure these distances as minimum arc-lengths along the
best-fitting circle (or min chord lengths, or min angular deviations with
respect to the centroid, etc)? This is how geographic distances are
measured (in 2 dimensions, rather than one) and clustered, and also how
distances are measured among observations in Kendall's shape spaces (e.g.,
Procrustes distances), so there's a well established literature.
Rich Strauss
At 05:32 PM 4/14/00 +0200, you wrote:
>Hi everybody.
>I face the problem of clustering one-dimensional data that can range in a
>circular way. Does anybody knows the best way to solve this problem with no
>aid of an additional variable ? Using a well-suitable trigonometric
>transform ? Using an ad-hoc metric ?
>Thanks.
>
>Carl
>
>
>
>
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========================
Dr Richard E Strauss
Biological Sciences
Texas Tech University
Lubbock TX 79409-3131
Email: [EMAIL PROTECTED]
Phone: 806-742-2719
Fax: 806-742-2963
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