Hi-
I am using Mean Shift clustering with good results. Mean Shift was chosen
because I don't know the number of clusters ahead of time, and the number of
samples is very small (<100) so performance is a non-issue.
Now I need to enforce an aging scheme, so that older samples influence the
clustering less than newer samples. My knowledge of clustering is limited,
but I'm looking for a way to weight the newer samples higher, such that the
algorithm tries harder to minimize their distance from the cluster centers
as compared to older samples.
>From looking through scikit-learn, I don't see a way to weight input samples
with Mean Shift or any other clustering algorithm. Google yielded several
papers on the subject but they quickly went over my head.
Does anyone know of a way to do this, either with a scikit-learn clustering
class or otherwise? Since performance is not a concern, I'd be open to any
hacky solutions, such as multiple rounds of clustering or filtering.
Thanks!
-Ben
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