[SciPy-dev] denoising spatial point process data
Fri Jan 9 15:28:46 CST 2009
On Fri, Jan 9, 2009 at 12:30, Sturla Molden <email@example.com> wrote:
>> 2009/1/9 Sturla Molden <firstname.lastname@example.org>:
>> As such, I wonder
>> whether it belongs more in with the clustering/machine learning code?
> It is not correct to call it a 'clustering' technique, but machine
> learning is an acceptable label. Similar to many clustering techniques
> (e.g. k-means) it fits a mixture model using the EM agorithm. It does not
> fit clusters, but separates target from clutter, based on the idea that
> clutter points will be widely scattered without adjacent neighbours.
> As for clustering:
> A clustering technique related to nnclean is 'superparamagnetic
> clustering'. It uses KNN distances and some form of MCMC (Swendsen-Wang or
> Wolff's algorithm). It is to my knowledge the only clustering method that
> is impervious to initialization, oblivious to the number of clusters in
> advance, can fit clusters of arbitrary shape, as well as guaranteed to
> converge to the globally correct solution. I have an implementation of
> that as well (it needs some more testing). Unfortunately it seems to be
> protected by a patent. I am not a lawyer, but it seems strange that a pure
> numerical method can be patented. At least in Europe, patents must include
> some sort of physical action, not just plain mathematics.
In the US, algorithms been patentable; the patents just need to be
written such that they refer to running it on a computer device. A
recent court decision has suggested that software patents that only
need a general computer rather than a more specific kind of hardware
may not be patentable, but we'll see how that holds up. Until that
decision gets some more support, I think we're still going to avoid
patented algorithms in scipy, though.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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