diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0a74885..0133dc9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -58,7 +58,7 @@
 
 # General information about the project.
 project = 'tsam'
-copyright = '2016-2020, tsam Developer Team'
+copyright = '2016-2021, tsam Developer Team'
 author = 'Leander Kotzur, Maximilian Hoffmann'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -66,9 +66,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.1.1'
+version = '1.1.2'
 # The full version, including alpha/beta/rc tags.
-release = '1.1.1'
+release = '1.1.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/docs/source/newsDoc.rst b/docs/source/newsDoc.rst
index 189b5d6..2d6f367 100644
--- a/docs/source/newsDoc.rst
+++ b/docs/source/newsDoc.rst
@@ -2,7 +2,29 @@
 tsam's Change Log
 #################
 
-In tsam's latest release (1.1.1) the following functionalities were included:
+*********************
+Release version 1.1.2
+*********************
+
+tsam release (1.1.2) includes the following new functionalities:
+
+* Added first version of the k-medoid contiguity algorithm.
+
+*********************
+Release version 1.1.1
+*********************
+
+tsam release (1.1.1) includes the following new functionalities:
 
 * Significantly increased test coverage 
 * Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen.
+
+*********************
+Release version 1.1.0
+*********************
+
+tsam release (1.1.0) includes the following new functionalities:
+
+* Segmentation - the clustering of adjacent time steps - according to Pineda et al. (2018)
+* k-MILP - an extension of the MILP-based k-medoids clustering that allows automatic identification of extreme periods according to Zatti et al. (2019)
+* The option to dynamically choose whether to clusters found should be represented by their centroid or medoid.