diff --git a/volia/clustering.py b/volia/clustering.py
index af8aefc..3093fc5 100644
--- a/volia/clustering.py
+++ b/volia/clustering.py
@@ -7,6 +7,7 @@ from sklearn.cluster import KMeans
 import pickle
 from clustering_modules.kmeans import kmeans
 from clustering_modules.kmeans_mahalanobis import  kmeansMahalanobis
+from clustering_modules.kmeans_multidistance import kmeansMultidistance
 
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics import v_measure_score, homogeneity_score, completeness_score
@@ -18,9 +19,13 @@ import json
 CLUSTERING_METHODS = {
     "k-means": kmeans(),
     "k-means-mahalanobis": kmeansMahalanobis(),
-    "k-means-mahalanobis-constrained": kmeansMahalanobis(constrained=True)
+    "k-means-mahalanobis-constrained": kmeansMahalanobis(constrained=True),
+    "k-means-basic-mahalanobis": kmeansMultidistance(distance="mahalanobis"),
+    "k-means-basic-cosine": kmeansMultidistance(distance="cosine")
 }
 
+KMEANS_METHODS = [key for key in CLUSTERING_METHODS if key.startswith("k-means")]
+
 EVALUATION_METHODS = {
     "entropy": core.measures.entropy_score,
     "purity": core.measures.purity_score,
@@ -77,8 +82,8 @@ def kmeans_run(features: str,
                ninit: int,
                output: str,
                tol: float,
-               debug: bool = False,
-               mahalanobis: str = False):
+               modeltype: str,
+               debug: bool = False):
     """
 
     @param features: output features
@@ -94,11 +99,7 @@ def kmeans_run(features: str,
     def fit_model(k: int, output_file):
         if debug:
             print(f"Computing clustering with k={k}")
-        model = CLUSTERING_METHODS["k-means"]
-        if mahalanobis:
-            if debug:
-                print("Mahalanobis activated")
-            model = CLUSTERING_METHODS["k-means-mahalanobis-constrained"]
+        model = CLUSTERING_METHODS[modeltype]
         model.fit(X, k, tol, ninit, maxiter, debug)
         model.save(output_file)
         json_content["models"].append({
@@ -193,7 +194,10 @@ if __name__ == "__main__":
     parser_kmeans.add_argument("--output",
                                default=".kmeans",
                                help="output file if only k. Output directory if multiple kmax specified.")
-    parser_kmeans.add_argument("--mahalanobis", action="store_true")
+    parser_kmeans.add_argument("--modeltype",
+                                required=True,
+                                choices=KMEANS_METHODS,
+                                help="type of model for learning")
     parser_kmeans.set_defaults(which="kmeans")
 
     # measure
@@ -223,7 +227,7 @@ if __name__ == "__main__":
     parser_disequilibrium.add_argument("--lstrain", required=True, type=str, help="...")
     parser_disequilibrium.add_argument("--lstest", required=True, type=str, help="...")
     parser_disequilibrium.add_argument("--model", required=True, type=str, help="...")
-    parser_disequilibrium.add_argument("--model-type",
+    parser_disequilibrium.add_argument("--modeltype",
                                 required=True,
                                 choices=["kmeans", "2", "3"],
                                 help="...")