diff --git a/kMedoids.py b/kMedoids.py
index 296a1b5..a6a422a 100644
--- a/kMedoids.py
+++ b/kMedoids.py
@@ -5,7 +5,7 @@ import seaborn as sns
 
 from numpy.random import choice, seed
 from sklearn.decomposition import PCA
-from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import Normalizer
 
 seed(42)
 
@@ -43,9 +43,8 @@ def is_finished(old_medoids, new_medoids):
 
 def kmedoids(num_samples, num_clusters):
     df = pd.read_csv('CC GENERAL.csv', index_col='CUST_ID')
-    df = df[:num_samples]
-    df_scaled = pd.DataFrame(MinMaxScaler().fit_transform(df))
-    df_scaled = df_scaled.fillna(0)
+    df = df[:num_samples].fillna(0)
+    df_scaled = pd.DataFrame(Normalizer().fit_transform(df))
 
     # initialize medoids (at random)
     medoids = initialize_medoids(num_medoids=num_clusters, data=df_scaled)
@@ -67,4 +66,6 @@ def kmedoids(num_samples, num_clusters):
     plt.show()
 
 
-kmedoids(num_samples=500, num_clusters=3)
+for i in range(2, 8):
+    kmedoids(num_samples=500, num_clusters=i)
+    print(i)
diff --git a/27.gif b/norm27.gif
similarity index 100%
rename from 27.gif
rename to norm27.gif
diff --git a/readme.md b/readme.md
index 9e34e9a..d8b0c05 100644
--- a/readme.md
+++ b/readme.md
@@ -6,6 +6,11 @@ and Cyber-Security at AMU Poznań.
 dataset:
 https://www.kaggle.com/arjunbhasin2013/ccdata
 
-![](27.gif)
+![](reg27.gif)
+
+Visualization of clustering first 500 data entries (with regularization).
+
+![](norm27.gif)
+
+Visualization of clustering first 500 data entries (with normalization).
 
-It may seem that the quality is not satisfactory for bigger numbers of cluster. That is due to the fact that the data has 17 dimensions and for the purpouse of plotting it is [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis)'ed into just 2 dimensions.
\ No newline at end of file
diff --git a/reg27.gif b/reg27.gif
new file mode 100644
index 0000000..e489c55
Binary files /dev/null and b/reg27.gif differ