diff --git a/scripts/evaluations/clustering.py b/scripts/evaluations/clustering.py
index 8d509b3..ead07be 100644
--- a/scripts/evaluations/clustering.py
+++ b/scripts/evaluations/clustering.py
@@ -18,7 +18,7 @@ from sklearn.metrics import f1_score, homogeneity_score, completeness_score, v_m
 import matplotlib.pyplot as plt
 
 from volia.data_io import read_features,read_lst
-from volia.measures import entropy_score
+from volia.measures import entropy_score, purity_score
 
 '''
 TODO: 
@@ -77,6 +77,12 @@ def train_clustering(label_encoder, feats, classes, outdir):
     # V-Measure
     v_measure = v_measure_score(labels, predicted_labels)
 
+    # Purity
+    purity_scores = purity_score(labels, predicted_labels)
+    purity_class_score = purity_scores["purity_class_score"]
+    purity_cluster_score = purity_scores["purity_cluster_score"]
+    K = purity_scores["K"]
+
     # Write results
     with open(os.path.join(outdir, f"_" + args.prefix + "eval_clustering.log"), "w") as fd:
         print(f"F1-scores for each classes:\n{fscores_str}", file=fd)
@@ -85,6 +91,9 @@ def train_clustering(label_encoder, feats, classes, outdir):
         print(f"Homogeneity: {homogeneity}", file=fd)
         print(f"completeness: {completeness}", file=fd)
         print(f"v-measure: {v_measure}", file=fd)
+        print(f"purity class score: {purity_class_score}", file=fd)
+        print(f"purity cluster score: {purity_cluster_score}", file=fd)
+        print(f"purity overall evaluation criterion (K): {K}", file=fd)
 
     # Process t-SNE and plot
     tsne_estimator = TSNE()
@@ -128,9 +137,13 @@ def train_clustering(label_encoder, feats, classes, outdir):
         "entropy": entropy,
         "homogeneity": homogeneity,
         "completeness": completeness,
-        "v-measure": v_measure
+        "v-measure": v_measure,
+        "purity_class_score": purity_class_score,
+        "purity_cluster score": purity_cluster_score,
+        "K": K
     }
 
+
 if __name__ == "__main__":
     # Argparse
     parser = argparse.ArgumentParser("Compute clustering on a latent space")