diff --git a/algorithms/linfa-clustering/src/k_means/algorithm.rs b/algorithms/linfa-clustering/src/k_means/algorithm.rs
index b03450919..ff2a40ee8 100644
--- a/algorithms/linfa-clustering/src/k_means/algorithm.rs
+++ b/algorithms/linfa-clustering/src/k_means/algorithm.rs
@@ -243,13 +243,11 @@ impl<F: Float, R: Rng + Clone, DA: Data<Elem = F>, T, D: Distance<F>>
         let n_runs = self.n_runs();
 
         for _ in 0..n_runs {
-            let mut inertia = min_inertia;
             let mut centroids =
                 self.init_method()
                     .run(self.dist_fn(), self.n_clusters(), observations, &mut rng);
-            let mut converged_iter: Option<u64> = None;
             let mut n_iter = 0;
-            while n_iter < self.max_n_iterations() {
+            let (inertia, converged_iter) = loop {
                 update_memberships_and_dists(
                     self.dist_fn(),
                     &centroids,
@@ -258,17 +256,15 @@ impl<F: Float, R: Rng + Clone, DA: Data<Elem = F>, T, D: Distance<F>>
                     &mut dists,
                 );
                 let new_centroids = compute_centroids(&centroids, &observations, &memberships);
-                inertia = dists.sum();
                 let distance = self
                     .dist_fn()
                     .distance(centroids.view(), new_centroids.view());
                 centroids = new_centroids;
                 n_iter += 1;
                 if distance < self.tolerance() || n_iter == self.max_n_iterations() {
-                    converged_iter = Some(n_iter);
-                    break;
+                    break (dists.sum(), Some(n_iter));
                 }
-            }
+            };
 
             // We keep the centroids which minimize the inertia (defined as the sum of
             // the squared distances of the closest centroid for all observations)