Fix a typo for k-means++
@@ -191,7 +191,7 @@ minimum. This is highly dependent on the initialization of the centroids.
As a result, the computation is often done several times, with different
initializations of the centroids. One method to help address this issue is the
k-means++ initialization scheme, which has been implemented in scikit-learn
-(use the ``init='kmeans++'`` parameter). This initializes the centroids to be
+(use the ``init='k-means++'`` parameter). This initializes the centroids to be
(generally) distant from each other, leading to provably better results than
random initialization, as shown in the reference.
