Fix malformed list in docs

Author: dmlls

docs/comparing_clustering_algorithms.rst

@@ -171,19 +171,21 @@ multiple different clusterings. This does not engender much confidence
 in any individual clustering that may result.
 
 So, in summary, here's how K-Means seems to stack up against out
-desiderata: 
-- **Don't be wrong!**: K-means is going to throw points
-into clusters whether they belong or not; it also assumes your clusters
-are globular. K-Means scores very poorly on this point.
-- **Intuitive parameters**: If you have a good intuition for how many clusters the
-dataset your exploring has then great, otherwise you might have a
-problem. 
-- **Stability**: Hopefully the clustering is stable for your
-data. Best to have many runs and check though. 
-- **Performance**: This is K-Means big win. It's a simple algorithm and with the right tricks
-and optimizations can be made exceptionally efficient. There are few
-algorithms that can compete with K-Means for performance. If you have
-truly huge data then K-Means might be your only option.
+desiderata:
+
+- **Don't be wrong!**: K-means is going to throw points into clusters
+  whether they belong or not; it also assumes your clusters are globular.
+  K-Means scores very poorly on this point.
+- **Intuitive parameters**: If you have a good intuition for how many
+  clusters the dataset your exploring has then great, otherwise you might
+  have a problem.
+- **Stability**: Hopefully the clustering is stable for your data. Best
+  to have many runs and check though.
+- **Performance**: This is K-Means big win. It's a simple algorithm and
+  with the right tricks and optimizations can be made exceptionally
+  efficient. There are few algorithms that can compete with K-Means for
+  performance. If you have truly huge data then K-Means might be your only
+  option.
 
 But enough opinion, how does K-Means perform on our test dataset? Let's
 have look. We'll be generous and use our knowledge that there are six