Minor edits

Author: firefly-cpp

README.md

@@ -16,7 +16,7 @@
 
 ## General outline of framework
 NiaARM is a framework for Association Rule Mining based on nature-inspired algorithms for optimization. The framework is fully written in Python and runs on all platforms.
-NaARM allows users to automatically preprocess the data in a transaction database, to search for association rules and provide pretty output of rules found. This framework also supports numerical and real-valued types of attributes besides the categorical ones. Mining the association rules is defined as an optimization and solved using the nature-inspired algorithms that comes from the related framework called [NiaPy](https://github.com/NiaOrg/NiaPy).
+NiaARM allows users to automatically preprocess the data in a transaction database, to search for association rules and provide pretty output of rules found. This framework also supports numerical and real-valued types of attributes besides the categorical ones. Mining the association rules is defined as an optimization problem and solved using the nature-inspired algorithms that comes from the related framework called [NiaPy](https://github.com/NiaOrg/NiaPy).
 
 ## Detailed insights
 Current version witholds (but is not limited to) following functions:
@@ -39,7 +39,7 @@ pip3 install niaarm
 
 ## Examples
 
-For a list of examples see the [examples folder]((examples/).
+For a list of examples see the [examples folder](examples/).
 
 ## Reference Papers:
 

datasets/README.md

@@ -0,0 +1,5 @@
+# References
+
+- Abalone dataset is downloaded from https://archive.ics.uci.edu/ml/index.php.
+
+- wiki_test_case dataset is composed from first Table found in: https://en.wikipedia.org/wiki/Lift_(data_mining)

niaarm/tests/test_shrinkage.py

@@ -34,15 +34,6 @@ def test_A(self):
         self.assertEqual(shrinkage, 1)
 
     def test_B(self):
-        # Rule: B => 1
-        # antecedent_b = [['B']]
-
-        # consequent_b = [[1, 1]]
-
-        # support_b = 0.2857142857142857
-
-        # confidence_b = 0.666666666667
-
         vector = [
             0.95157038,
             0.17362622,