diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index b990159820f92b4c23b975650738c06c49d7bb41..2b6ec645b6bd8b6fe1ae2128e7fd7125bb8dce7e 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -565,13 +565,15 @@ Mathematical formulation
 ~~~~~~~~~~~~~~~~~~~~~~~~
 Assume two label assignments (of the same data), :math:`U` with :math:`R`
 classes and :math:`V` with :math:`C` classes. The entropy of either is the
- amount of uncertaintly for an array, and can be calculated as:
+amount of uncertaintly for an array, and can be calculated as:
 
 .. math:: H(U) = \sum_{i=1}^{|R|}P(i)log(P(i))
 
 Where P(i) is the number of instances in U that are in class :math:`R_i`.
 Likewise, for :math:`V`:
+
 .. math:: H(V) = \sum_{j=1}^{|C|}P'(j)log(P'(j))
+
 Where P'(j) is the number of instances in V that are in class :math:`C_j`.
 
 The (non-adjusted) mutual information between :math:`U` and :math:`V` is
diff --git a/doc/modules/mixture.rst b/doc/modules/mixture.rst
index 5b32ccecd0fa97d57bbfb6be42868b3eb8d189b9..c1a97bf5c22714855140522ac2f609ac594e9603 100644
--- a/doc/modules/mixture.rst
+++ b/doc/modules/mixture.rst
@@ -47,7 +47,7 @@ only needs to specify a loose upper bound on this number and a
 concentration parameter.
 
 Expectation-maximization
------------------------
+------------------------
 
 The main difficulty in learning gaussian mixture models from unlabeled
 data is that it is one usually doesn't know which points came from