change normalization to regularization, new gif

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)

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.