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doc/modules/clustering.rst
@@ -202,7 +202,7 @@ As a result, the computation is often done several times, with different
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initializations of the centroids. One method to help address this issue is the
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k-means++ initialization scheme, which has been implemented in scikit-learn
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(use the ``init='k-means++'`` parameter). This initializes the centroids to be
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-(generally) distant from each other, leading to provably better results than
+(generally) distant from each other, leading to probably better results than
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random initialization, as shown in the reference.
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K-means++ can also be called independently to select seeds for other
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