Hi Robert,
As I see in the current implementation of DBSCAN, if the metric is not
'precomputed', then a nearest neighbor model is trained with the existing
implementation of neighbors module. What I meant is since this ANN search
will also be implemented similar (because it must adhere the API of
neighbors module) to those exact neighbor search methods, I think it will
not be much of a problem to apply ANN in DBSCAN.
On Wed, Apr 16, 2014 at 4:03 AM, Robert Layton <[email protected]>wrote:
> I wrote the original DBSCAN, in a time before I knew anything about sparse
> matrices (I know now a little), so there may be artefacts in there that
> aren't scalable -- i.e. a separate iteration over the array for something
> or an operation that copies the matrix.
> It has since been updated though, and I haven't had a chance to check out
> the new code.
>
> The reason I say this is that if you improve ANN, you might get a cheap
> improvement in the other algorithms, but it would be worth ensuring that
> the rest of the code can "handle" the increased scale.
>
>
> On 16 April 2014 00:39, Maheshakya Wijewardena <[email protected]>wrote:
>
>> 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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>
>
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--
Undergraduate,
Department of Computer Science and Engineering,
Faculty of Engineering.
University of Moratuwa,
Sri Lanka
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