Both mean-shift and dbscan directly use
`sklearn.neighbors.NearestNeighbors` to train models and get nearest
neighbors, unlike k-means. So I suppose, as the ANN will also act similar
to Nearest neighbors, it can be used in that place without having to change
the usage or semantics of those clustering methods.
On Fri, Apr 11, 2014 at 3:24 PM, Lars Buitinck <[email protected]> wrote:
> 2014-04-11 10:55 GMT+02:00 Daniel Vainsencher <
> [email protected]>:
> > In any case, the approximate nature of the search raises the possibility
> > of going a step further: index the data points, and adjust each cluster
> > to its ANNs (in this case, for a very long list of candidates). This is
> > no longer k-means (closer to a mean-shift algorithm) and may or may not
> > work, but could be very fast.
>
> Speaking of, mean-shift is already implemented using NN. Judging from
> GitHub issues, ML questions and the complexity notes in the mean-shift
> docstrings, I also believe that optimizing it would be more valuable
> than optimizing k-means, since we already have minibatch k-means.
>
> (Also k-means can still benefit from the Elkan optimization, which
> doesn't change its semantics.)
>
>
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--
Undergraduate,
Department of Computer Science and Engineering,
Faculty of Engineering.
University of Moratuwa,
Sri Lanka
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