Good to hear. You are right -- you should follow the API closely, deviating
only if necessary -- the consistent API is a core part of the popularity of
scikit-learn.
On 17 April 2014 06:51, Maheshakya Wijewardena <[email protected]>wrote:
> 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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>
>
> --
> Undergraduate,
> Department of Computer Science and Engineering,
> Faculty of Engineering.
> University of Moratuwa,
> Sri Lanka
>
>
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> "Graph Databases" is the definitive new guide to graph databases and their
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