diff --git a/src/compute/models/k_means.rs b/src/compute/models/k_means.rs
index b76fe51..83412e3 100644
--- a/src/compute/models/k_means.rs
+++ b/src/compute/models/k_means.rs
@@ -162,6 +162,45 @@ impl KMeans {
 
 #[cfg(test)]
 mod tests {
+    #[test]
+    fn test_k_means_empty_cluster_reinit_centroid() {
+        // Try multiple times to increase the chance of hitting the empty cluster case
+        for _ in 0..20 {
+            let data = vec![0.0, 0.0, 0.0, 0.0, 10.0, 10.0];
+            let x = FloatMatrix::from_rows_vec(data, 3, 2);
+            let k = 2;
+            let max_iter = 10;
+            let tol = 1e-6;
+
+            let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
+
+            // Check if any cluster is empty
+            let mut counts = vec![0; k];
+            for &label in &labels {
+                counts[label] += 1;
+            }
+            if counts.iter().any(|&c| c == 0) {
+                // Only check the property for clusters that are empty
+                let centroids = kmeans_model.centroids;
+                for c in 0..k {
+                    if counts[c] == 0 {
+                        let mut matches_data_point = false;
+                        for i in 0..3 {
+                            let dx = centroids[(c, 0)] - x[(i, 0)];
+                            let dy = centroids[(c, 1)] - x[(i, 1)];
+                            if dx.abs() < 1e-9 && dy.abs() < 1e-9 {
+                                matches_data_point = true;
+                                break;
+                            }
+                        }
+                        assert!(matches_data_point, "Centroid {} (empty cluster) does not match any data point", c);
+                    }
+                }
+                break;
+            }
+        }
+        // If we never saw an empty cluster, that's fine; the test passes as long as no panic occurred
+    }
     use super::*;
     use crate::matrix::FloatMatrix;