Yes. I agree. But after all, we cannot guarantee that any of the clustering
algorithms will perform well on every setting of data. So don't you think
it is worth trying to apply ANN methods on these applications?
On Fri, Apr 11, 2014 at 2:25 PM, Daniel Vainsencher <
[email protected]> wrote:
> Indexing the clusters has the down side that the clusters change over
> time, requiring the index to be reconstructed. This might be a speed up
> or not, depending on the relation of clusters to data point cardinality
> and on the exact speed of data structures.
>
> 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.
>
> Daniel
>
> On 04/11/2014 09:39 AM, Maheshakya Wijewardena wrote:
> > Is that so? I'm sorry. I haven't checked the implementation of
> > hierarchical clustering yet.
> > What do you think about the approach I suggested for k-means clustering?
> >
> >
> > On Fri, Apr 11, 2014 at 12:02 PM, Gael Varoquaux
> > <[email protected] <mailto:[email protected]>>
> > wrote:
> >
> > > I think we can improve hierarchical clustering and dbscan as well
> > with
> > > approximate neighbor search.
> >
> > Hierarchical clustering, I don't think so, because we don't have
> (yet)
> > single linkage, which is what would benefit from it.
> >
> >
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Sri Lanka
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