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.
Someone suggested that I look at time series clustering methods.
On Fri, Mar 22, 2013 at 10:17 AM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> 2013/3/22 Albert Kottke <albert.kot...@gmail.com>:
> > My biggest question is forming the data into the X matrix (n_samples,
> > n_features). The approach you describe would cluster based on thickness
> and
> > velocity without consideration of the relationship between adjacent
> layers.
> > Initially, I want to try to cluster based on change in Vs with depth. In
> > doing so, it is important that layer sequence is considered. Eventually,
> I
> > might want to consider other aspects, but I think this (Vs and depth)
> will
> > give me a good understanding of what is possible.
>
> I'm not sure what exactly you're trying to say, but in general, if you
> want to cluster based on some feature, then you should add a column
> with the feature's value to your X.
>
> So, switching to a completely fictitious problem, if you'd have to
> features i and j in your dataset as it's stored on disk, but you're
> actually interested in log(x[i]) / cos(x[j])² (again, I'm making this
> up), then just compute that quantity for all your samples and append
> the result as a column to your dataset.
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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