I think we can improve hierarchical clustering and dbscan as well with
approximate neighbor search. I will update you after I have investigated
those implementations further.
Thank you.
On Thu, Apr 10, 2014 at 4:12 AM, Robert Layton <[email protected]>wrote:
> I think you are right. Updating k-means to be more effecient has a number
> of flow-on effects like, as you say, improving EM in turn.
>
>
> On 10 April 2014 02:58, Maheshakya Wijewardena <[email protected]>wrote:
>
>> Hi,
>>
>> Currently in scikit-learn, Expectation maximization algorithm is used in
>> K-means clustering model to determine optimal cluster centers and labels.
>> In my opinion, the best place to apply LSH based ANN methods(proposed as a
>> GSOC project) is at the E step of the EM algorithm. The assignments of each
>> data point are determined at that step for the current setting of cluster
>> centers.
>> ANN search can be applied to find nearest cluster centers of each data
>> point. In `sklearn.cluster.k_means_.py`, from `_labels_inertia` function,
>> the assignments are calculated using `_assign_labels_array` and
>> `_assign_labels_csr` functions. These functions choose the center with
>> minimum euclidean distance. Instead of that, from an ANN search, nearest
>> neighbors can be approximated.
>>
>> This is my current plan for this. Your feedback is welcome.
>>
>> Best regards,
>> Maheshakya
>> --
>> 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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