[scikit-learn] Using KMeans cluster labels in KNN

[Joel Nothman]

A meta-estimator for this (generic to which classifier / clusterer) is
coded up at
https://github.com/scikit-learn/scikit-learn/issues/4543#issuecomment-91073246

We even have a pull request that made an example of this sort of thing at
https://github.com/scikit-learn/scikit-learn/pull/6478, but the original
contributor never responded to comments on it. If someone would like to
make it more persuasive and complete it, ...

On 13 March 2018 at 02:34, prince gosavi <princegosavi12 at gmail.com> wrote:

> Hi,
> Thank you for reply.
>
> I was exploring the possibility that given well formed KMean clusters
> using an additional KNN we can simply increase the accuracy that the data
> point enters the right cluster.
>
> Also I would like to know whether if it's possible to do such thing(out of
> curiosity)?
>
>
> On Mon, Mar 12, 2018 at 4:16 PM, Sebastian Raschka <se.raschka at gmail.com>
> wrote:
>
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>>
>> Hi,
>> If you want to predict the Kmeans cluster membership, you can use Kmeans'
>> predict method instead of training a KNN model on the cluster assignments.
>> This will be computationally more efficient and give you the correct
>> assignment at the borders between clusters.
>>
>> Best,
>> Sebastian
>>
>> > On Mar 12, 2018, at 2:55 AM, prince gosavi <princegosavi12 at gmail.com>
>> wrote:
>> >
>> > Hi,
>> > I have generated clusters using the KMeans algorithm and would like to
>> use the labels of the model in the KNN.
>> >
>> > I don't have the implementation idea but I can visualize it as
>> >
>> > KNNmodel = KNN.fit(X, KMeansModel.labels_)
>> >
>> > Such that the KNN will predict the cluster the new point belong to.
>> >
>> > --
>> > Regards
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>
>
> --
> Regards
>
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