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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