diff --git a/volia/clustering.py b/volia/clustering.py
index 7b2359f..8c4a98d 100644
--- a/volia/clustering.py
+++ b/volia/clustering.py
@@ -1,11 +1,51 @@
 import argparse
 from os import path, mkdir
 from utils import SubCommandRunner
-from core.data import read_features, read_lst
-
+from core.data import read_features, read_lst, read_labels
 import numpy as np
 from sklearn.cluster import KMeans
 import pickle
+from clustering_modules.kmeans import kmeans
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import v_measure_score
+
+import core.measures
+
+
+CLUSTERING_METHODS = {
+    "k-means": kmeans()
+}
+
+EVALUATION_METHODS = {
+    "entropy": core.measures.entropy_score,
+    "v-measure": v_measure_score
+}
+
+
+def disequilibrium_run():
+    pass
+
+
+def measure_run(measure: str, features: str, lst: str, truelabels: str, model: str, modeltype: str):
+    module = CLUSTERING_METHODS[modeltype]
+    module.load(model)
+    evaluation = EVALUATION_METHODS[measure]
+    feats_dict = read_features(features)
+    labels_dict = read_labels(truelabels)
+    lst_dict = read_lst(lst)
+    lst_keys = [key for key in lst_dict]
+    feats = np.asarray([feats_dict[key] for key in lst_keys])
+    Y_pred = module.predict(feats)
+    Y_truth = [labels_dict[key][0] for key in lst_keys]
+
+    le = LabelEncoder()
+    le.fit(Y_truth)
+    Y_truth = le.transform(Y_truth)
+
+    eval = evaluation(Y_truth, Y_pred)
+    print(eval)
+
 
 
 def kmeans_run(features: str, lst: str, k:int, kmax: int, klist, output: str):
@@ -18,7 +58,7 @@ def kmeans_run(features: str, lst: str, k:int, kmax: int, klist, output: str):
     @param klist: list of k values to compute, ignore k value
     @param output: output file if kmax not specified, else, output directory
     """
-    # -- READE FILES --
+    # -- READ FILES --
     features_dict = read_features(features)
     lst_dict = read_lst(lst)
     X = np.asarray([features_dict[x] for x in lst_dict])
@@ -79,12 +119,46 @@ if __name__ == "__main__":
     parser_kmeans.add_argument("--output", default=".kmeans", help="output file if only k. Output directory if multiple kmax specified.")
     parser_kmeans.set_defaults(which="kmeans")
 
+    # measure
+    parser_measure = subparsers.add_parser(
+        "measure", help="compute the entropy")
+
+    parser_measure.add_argument("--measure",
+                                required=True,
+                                type=str,
+                                choices=[key for key in EVALUATION_METHODS],
+                                help="...")
+    parser_measure.add_argument("--features", required=True, type=str, help="...")
+    parser_measure.add_argument("--lst", required=True, type=str, help="...")
+    parser_measure.add_argument("--truelabels", required=True, type=str, help="...")
+    parser_measure.add_argument("--model", required=True, type=str, help="...")
+    parser_measure.add_argument("--modeltype",
+                                required=True,
+                                choices=[key for key in CLUSTERING_METHODS],
+                                help="type of model for learning")
+    parser_measure.set_defaults(which="measure")
+
+    # disequilibrium
+    parser_disequilibrium = subparsers.add_parser(
+        "disequilibrium", help="...")
+
+    parser_disequilibrium.add_argument("--features", required=True, type=str, help="...")
+    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",
+                                required=True,
+                                choices=["kmeans", "2", "3"],
+                                help="...")
+
     # Parse
     args = parser.parse_args()
 
     # Run commands
     runner = SubCommandRunner({
-        "kmeans": kmeans_run
+        "kmeans": kmeans_run,
+        "measure": measure_run,
+        "disequilibrium": disequilibrium_run
     })
 
-    runner.run(args.which, args.__dict__, remove="which")
\ No newline at end of file
+    runner.run(args.which, args.__dict__, remove="which")
diff --git a/volia/clustering_modules/kmeans.py b/volia/clustering_modules/kmeans.py
new file mode 100644
index 0000000..2e58f70
--- /dev/null
+++ b/volia/clustering_modules/kmeans.py
@@ -0,0 +1,14 @@
+
+from sklearn.cluster import KMeans
+import pickle
+from abstract_clustering import AbstractClustering
+
+class kmeans():
+    def __init__(self):
+        self.kmeans_model = None
+
+    def predict(self, features):
+        return self.kmeans_model.predict(features)
+
+    def load(self, model_path):
+        self.kmeans_model = pickle.load(open(model_path, "rb"))
diff --git a/volia/core/measures.py b/volia/core/measures.py
index 0ef8967..4e5e94a 100644
--- a/volia/core/measures.py
+++ b/volia/core/measures.py
@@ -148,7 +148,7 @@ def entropy_score(y_truth, y_hat):
     
     result = result_vector * dividers / dividers.sum()
     result = result.sum()
-    return (result_matrix, result_vector, result)
+    return result
 
 
 def purity_score(y_truth, y_hat):
@@ -181,7 +181,7 @@ def purity_score(y_truth, y_hat):
         vector_purity = np.sum(matrix_divided, axis=axis)
 
         scalar_purity = np.average(vector_purity, weights=count_per_row)
-        return (vector_purity, scalar_purity)
+        return scalar_purity
     
 
     count_matrix = compute_count_matrix(y_truth, y_hat)