diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index b27496944a616..6842c1e2bde4f 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -301,7 +301,9 @@ is given.
 Affinity Propagation can be interesting as it chooses the number of
 clusters based on the data provided. For this purpose, the two important
 parameters are the *preference*, which controls how many exemplars are
-used, and the *damping factor*.
+used, and the *damping factor* which damps the responsibility and 
+availability messages to avoid numerical oscillations when updating these
+messages.
 
 The main drawback of Affinity Propagation is its complexity. The
 algorithm has a time complexity of the order :math:`O(N^2 T)`, where :math:`N`
@@ -350,6 +352,13 @@ to be the exemplar of sample :math:`i` is given by:
 
 To begin with, all values for :math:`r` and :math:`a` are set to zero,
 and the calculation of each iterates until convergence.
+As discussed above, in order to avoid numerical oscillations when updating the 
+messages, the damping factor :math:`\lambda` is introduced to iteration process:
+
+.. math:: r_{t+1}(i, k) = \lambda\cdot r_{t}(i, k) + (1-\lambda)\cdot r_{t+1}(i, k)
+.. math:: a_{t+1}(i, k) = \lambda\cdot a_{t}(i, k) + (1-\lambda)\cdot a_{t+1}(i, k)
+
+where :math:`t` indicates the iteration times.
 
 .. _mean_shift:
 
diff --git a/sklearn/cluster/affinity_propagation_.py b/sklearn/cluster/affinity_propagation_.py
index 398529793880f..8bf94cee95cda 100644
--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -197,7 +197,11 @@ class AffinityPropagation(BaseEstimator, ClusterMixin):
     Parameters
     ----------
     damping : float, optional, default: 0.5
-        Damping factor between 0.5 and 1.
+        Damping factor (between 0.5 and 1) is the extent to
+        which the current value is maintained relative to
+        incoming values (weighted 1 - damping). This in order
+        to avoid numerical oscillations when updating these
+        values (messages).
 
     max_iter : int, optional, default: 200
         Maximum number of iterations.