[scikit-learn] Applying clustering to cosine distance matrix
princegosavi12 at gmail.com
Mon Feb 12 16:29:21 EST 2018
Thanks for those tips Sebastian.That just saved my day.
On Tue, Feb 13, 2018 at 12:44 AM, Sebastian Raschka <se.raschka at gmail.com>
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> by default, the clustering classes from sklearn, (e.g., DBSCAN), take an
> [num_examples, num_features] array as input, but you can also provide the
> distance matrix directly, e.g., by instantiating it with
> my_dbscan = DBSCAN(..., metric='precomputed')
> Not sure if it helps in that particular case (depending on how many zero
> elements you have), you can also use a sparse matrix in CSR format (
> Also, you don't need to for-loop through the rows if you want to compute
> the pair-wise distances, you can simply do that on the complete array. E.g.,
> from sklearn.metrics.pairwise import cosine_distances
> from scipy import sparse
> distance_matrix = cosine_distances(sparse.csr_matrix(X),
> where X is your "[num_examples, num_features]" array.
> > On Feb 12, 2018, at 1:10 PM, prince gosavi <princegosavi12 at gmail.com>
> > I have generated a cosine distance matrix and would like to apply
> clustering algorithm to the given matrix.
> > np.shape(distance_matrix)==(14000,14000)
> > I would like to know which clustering suits better and is there any need
> to process the data further to get it in the form so that a model can be
> > Also any performance tip as the matrix takes around 3-4 hrs of
> > You can find my code here https://github.com/
> > Code for READ ONLY PURPOSE.
> > --
> > Regards
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