DOC Fix typo in Clustering page
@@ -141,7 +141,7 @@ K-means
The :class:`KMeans` algorithm clusters data by trying to separate samples in n
groups of equal variance, minimizing a criterion known as the *inertia* or
within-cluster sum-of-squares (see below). This algorithm requires the number
-of clusters to be specified. It scales well to large number of samples and has
+of clusters to be specified. It scales well to large numbers of samples and has
been used across a large range of application areas in many different fields.
The k-means algorithm divides a set of :math:`N` samples :math:`X` into
