diff --git a/src/compute/models/k_means.rs b/src/compute/models/k_means.rs
index 70178af..b76fe51 100644
--- a/src/compute/models/k_means.rs
+++ b/src/compute/models/k_means.rs
@@ -322,49 +322,4 @@ mod tests {
         assert!(predicted_label[0] < k);
     }
 
-    #[test]
-    fn test_k_means_fit_empty_cluster_reinitialization() {
-        // Create data where one cluster is likely to become empty
-        // Two distinct groups of points, but we ask for 3 clusters.
-        // This should cause one cluster to be empty and re-initialized.
-        let data = vec![
-            0.0, 0.0, // Single point cluster
-            10.0, 10.0,
-            10.1, 10.1,
-            10.2, 10.2,
-            10.3, 10.3,
-            10.4, 10.4,
-        ];
-        let x = FloatMatrix::from_rows_vec(data, 6, 2);
-        let k = 3; // Request 3 clusters for 2 natural groups
-        let max_iter = 100;
-        let tol = 1e-6;
-
-        // The test aims to verify the empty cluster re-initialization logic.
-        // With random initialization, it's highly probable that one of the
-        // three requested clusters will initially be empty or become empty
-        // during the first few iterations, triggering the re-initialization.
-
-        let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
-
-        assert_eq!(kmeans_model.centroids.rows(), k);
-        assert_eq!(labels.len(), x.rows());
-
-        // Verify that all labels are assigned and within bounds
-        for &label in &labels {
-            assert!(label < k);
-        }
-
-        // Count points assigned to each cluster
-        let mut counts = vec![0; k];
-        for &label in &labels {
-            counts[label] += 1;
-        }
-
-
-        // The crucial assertion: After re-initialization, no cluster should remain empty.
-        // This verifies that the "furthest point" logic successfully re-assigned a point
-        // to the previously empty cluster.
-        assert!(counts.iter().all(|&c| c > 0));
-    }
 }