Hi Albert
On Fri, Mar 22, 2013 at 6:33 PM, Albert Kottke <albert.kot...@gmail.com>wrote:
> Consider the following profiles:
>
> # A B C D
> 1 10 9 3 2
> 2 4 5 4 5
> 3 6 5 6 7
>
> I have removed thickness and just used layer number for simplicity. The
> desired behavior is that profiles A and B are grouped together because they
> start high, decrease, and then slightly increase. Profiles C and D should
> also be grouped together since they both gradually increase. This grouping
> only works if the complete profile is considered.
>
By convention in sklearn you have rows that represents samples
while columns rapresent features.
Clustering algorithms in sklearn cluster "rows".
In your example you are trying to group (cluster) columns.
In order to leverage sklearn for clustering you need to transponse the
matrix [1]
basically your samples are profiles and each sample's features are the
measurement
of the given quantity at the given time.
Hopes this helps
Paolo
[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.T.html
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