DOC Use :doi: directive for KMeans
@@ -1287,8 +1287,9 @@ class KMeans(_BaseKMeans):
samples and T is the number of iteration.
The worst case complexity is given by O(n^(k+2/p)) with
- n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii,
- 'How slow is the k-means method?' SoCG2006)
+ n = n_samples, p = n_features.
+ Refer to :doi:`"How slow is the k-means method?" D. Arthur and S. Vassilvitskii -
+ SoCG2006.<10.1145/1137856.1137880>` for more details.
In practice, the k-means algorithm is very fast (one of the fastest
clustering algorithms available), but it falls in local minima. That's why
