DOC improved readability of instructions
@@ -4,18 +4,18 @@ Empirical evaluation of the impact of k-means initialization
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Evaluate the ability of k-means initializations strategies to make
-the algorithm convergence robust as measured by the relative standard
+the algorithm convergence robust, as measured by the relative standard
deviation of the inertia of the clustering (i.e. the sum of squared
distances to the nearest cluster center).
The first plot shows the best inertia reached for each combination
-of the model (``KMeans`` or ``MiniBatchKMeans``) and the init method
+of the model (``KMeans`` or ``MiniBatchKMeans``), and the init method
(``init="random"`` or ``init="kmeans++"``) for increasing values of the
``n_init`` parameter that controls the number of initializations.
-The second plot demonstrate one single run of the ``MiniBatchKMeans``
+The second plot demonstrates one single run of the ``MiniBatchKMeans``
estimator using a ``init="random"`` and ``n_init=1``. This run leads to
-a bad convergence (local optimum) with estimated centers stuck
+a bad convergence (local optimum), with estimated centers stuck
between ground truth clusters.
The dataset used for evaluation is a 2D grid of isotropic Gaussian
