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 additional variable ? Using a well-suitable trigonometric
>> transform ? Using an ad-hoc metric ?
>> Thanks.
>If you mean that data are constrained to lie on a circle of fixed radius,
>you could consider substituting the distance of each point from the next
>one (in one direction or the other). This would provide global
>information (sorry -- pun unintentional) about the existence of
>clustering. Plotting this against, say, angular dispacement would give
>information about the location(s) of cluster(s).
This is a poor procedure, even with a fair amount of clustering.
Clustering is a poorly defined concept in the first place. But
a linear procedure, with some care not to split the circle in
the "middle" of a cluster, should do just about as well. This
is not the same as finding a combination of normal or near-normal
distributions; a relatively flat stretch with drops at the edges
is going to require many of them with a fairly large sample size,
although it looks like one cluster.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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