diff --git a/code/unsupervised_DT/decision_tree.py b/code/unsupervised_DT/decision_tree.py
index bfba35be1143836458dd6cf1a684add864da03cd..a2f1a73bd8b693b76627a5608f64d45d9699d20f 100644
--- a/code/unsupervised_DT/decision_tree.py
+++ b/code/unsupervised_DT/decision_tree.py
@@ -302,8 +302,8 @@ if __name__ == '__main__':
 min_users = 50
 os.getcwd()
 path = os.path.dirname(os.path.realpath(__file__))
-print(path+ "/dt/train_embed.csv")
-H_users = fromDFtoArray(path+ "/dt/train_embed.csv",False,'f')
+print(path+ "/files_for_dt/train_embed.csv")
+H_users = fromDFtoArray(path+ "/files_for_dt/train_embed.csv",False,'f')
 dim = H_users.shape[1]
 n = H_users.shape[0]
 
@@ -327,7 +327,7 @@ for i in range(len(partitions)):
     writer.writerow(row)
 f.close()
 '''
-partitions_init = fromDFtoArray(path+ "/dt/train_partitions.csv",False,'f')
+partitions_init = fromDFtoArray(path+ "/files_for_dt/train_partitions.csv",False,'f')
 
 # Step 2: take the partition with max silhouette value
 best_init_part = np.argmax(partitions_init[:,2])
@@ -373,7 +373,7 @@ for i in range(1,len(theClusters)):
 
 # Validation with questionnaires
 print("")
-file = path+ "/dt/train_user_quest_label.csv"
+file = path+ "/files_for_dt/train_user_quest_label.csv"
 quest = fromDFtoArray(file,False,'f')
 questClusters = []
 for i in range(len(quest)):
@@ -392,4 +392,4 @@ compare_clusters(dtClust, questClusters, quest)
 
 
 #print(partitions_init)
-    
\ No newline at end of file
+