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
In article <[EMAIL PROTECTED]>,
Donald F. Burrill <[EMAIL PROTECTED]> wrote:
>On Fri, 14 Apr 2000, Carl Frelicot wrote:
>> 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 addition
On Fri, 14 Apr 2000, Carl Frelicot wrote:
> 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 m
If your data can be "cut" and unrolled at a specific boundry then you
can rotate/translate the data away from the boundry. For example if
your data crosses the 0 degree boundry but not the -180/+180 boundry
then you don't need to do anything, if it crosses the -180/+180
boundry but not the 0 degr
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
===