[R-sig-Geo] complete linkage Agglomerative hierarchical clustering, nnclust, spatclus or something else?

[Hans Ekbrand]

On Tue, Apr 20, 2010 at 11:13:22PM +0200, Hans Ekbrand wrote:
> Roger Bivand wrote:
> > On Tue, 20 Apr 2010, Hans Ekbrand wrote:
> >
> >> I have just read about clustering on wikipedia, and learnt that what I
> >> want is:
> >>
> >> Agglomerative hierarchical clustering, with complete linkage
> >
> > library(cluster)
> > ?hclust

print(load(url("http://sociologi.cjb.net/temp/clust.geo.test.RData")))
clust.geo.test.tree <- hclust(dist(clust.geo.test at coords))
clust.geo.test.tree$height

head(clust.geo.test.tree$height, 70)
 [1]   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
[11]   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
[21]   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
[31]   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
[41]   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
[51]   0.000000   0.000000   0.000000   0.000000   3.160631  18.963676  30.398644  32.232351  37.927539  44.987446
[61]  50.065192  81.542472  82.691738  93.553729  95.971207 105.325405 115.218371 119.540239 125.235381 130.181302

As I understand this, the 54 zeroes represent identical coordinates.
The positive numbers represent the distance in meters between points
that have been grouped together at a certain level of the tree. Now, I
am not interested in grouping together points with distances larger
than 100 meters, so I would like to stop the clustering process at
that point - or, after the hclust has completed, extract the clusters
that were in effect at that level. In the above example that would be
at level 65.

I didn't understand from the documentation of hclust how to accomplish
that, can someone on the list help me?

The goal is to count, for each cluster, the number of fires and then
to analyse how the fires within each cluster is distributed over time,
and to count how many of them that are too close in time to be
considered independent.
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