diff --git a/scripts/evaluations/clustering.py b/scripts/evaluations/clustering.py
index 6b5e136..7c6e1eb 100644
--- a/scripts/evaluations/clustering.py
+++ b/scripts/evaluations/clustering.py
@@ -30,27 +30,34 @@ clustering to train in order to compute the average and the
 
 def train_clustering(label_encoder, feats, classes, outdir):
     num_classes = len(label_encoder.classes_)
-
-    # Compute KMEANS clustering on data
-    kmeans_parameters = {
-        "n_clusters": num_classes,
-        "n_init": 100,
-        "tol": 10-6,
-        "algorithm": "elkan"
-    }
-    with open(os.path.join(outdir, f"{args.prefix}kmeans_parameters.json"), "w") as f:
-        json.dump(kmeans_parameters, f)
-
-    # Save parameters
-    
-    estimator = KMeans(
-        **kmeans_parameters
-    )
-    estimator.fit(feats)
-    print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")
-
-    with open(os.path.join(outdir, f"{args.prefix}kmeans.pkl"), "wb") as f:
-        pickle.dump(estimator, f)
+    estimator = None
+    kmeans_filepath = os.path.join(outdir, f"{args.prefix}kmeans.pkl") 
+    if args.onlymeasures:
+        print(f"Loading model: {kmeans_filepath}")
+        with open(kmeans_filepath, "rb") as f:
+            estimator = pickle.load(f)
+    else:
+        # Compute KMEANS clustering on data
+        print("Saving parameters")
+        kmeans_parameters = {
+            "n_clusters": num_classes,
+            "n_init": 100,
+            "tol": 10-6,
+            "algorithm": "elkan"
+        }
+        with open(os.path.join(outdir, f"{args.prefix}kmeans_parameters.json"), "w") as f:
+            json.dump(kmeans_parameters, f)
+
+        # Fit the model and Save parameters
+        print(f"Fit the model: {kmeans_filepath}")
+        estimator = KMeans(
+            **kmeans_parameters
+        )
+        estimator.fit(feats)
+        print(f"Kmeans: processed {estimator.n_iter_} iterations - intertia={estimator.inertia_}")
+
+        with open(kmeans_filepath, "wb") as f:
+            pickle.dump(estimator, f)
     
     # contains distance to each cluster for each sample
     dist_space = estimator.transform(feats)
@@ -179,7 +186,10 @@ if __name__ == "__main__":
     parser.add_argument("--nmodels",
                         type=int,
                         default=1,
-                        help="specifies the number of models to train")   
+                        help="specifies the number of models to train")
+    parser.add_argument("--onlymeasures",
+                        action='store_true',
+                        help="Don't compute the clustering, compute only the measures")
     args = parser.parse_args()
 
     assert args.outdir