diff --git a/rust/index/src/spann/utils.rs b/rust/index/src/spann/utils.rs
index d7400566b65..bd2035e8bcc 100644
--- a/rust/index/src/spann/utils.rs
+++ b/rust/index/src/spann/utils.rs
@@ -24,6 +24,7 @@ struct KMeansAlgorithmInput<'referred_data> {
 }
 
 #[allow(dead_code)]
+#[derive(Debug)]
 pub struct KMeansAlgorithmOutput {
     cluster_centers: Vec<Vec<f32>>,
     cluster_counts: Vec<usize>,
@@ -367,9 +368,9 @@ impl<'referred_data> KMeansAlgorithm<'referred_data> {
         let mut previous_counts = vec![];
         for _ in 0..NUM_ITERS_FOR_MAIN_LOOP {
             // Prepare for the next iteration.
-            let previous_centers = current_centers;
-            let previous_counts = current_counts;
-            self.input.indices.shuffle(&mut rand::thread_rng());
+            previous_centers = current_centers;
+            previous_counts = current_counts;
+            self.input.indices[self.input.first..self.input.last].shuffle(&mut rand::thread_rng());
             let mut kmeans_assign = self.kmeansassign_for_main_loop(
                 &previous_centers,
                 &previous_counts,
@@ -429,17 +430,18 @@ mod tests {
         // 2D embeddings.
         let dim = 2;
         let embeddings = [
-            0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 10.0, 10.0, 11.0, 10.0, 10.0, 11.0, 11.0, 11.0,
+            -1.0, -1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 10.0, 10.0, 11.0, 10.0, 10.0, 11.0,
+            11.0, 11.0, 12.0, 12.0,
         ];
-        let indices: Vec<u32> = vec![0, 1, 2, 3, 4, 5, 6, 7];
+        let indices: Vec<u32> = vec![0, 1, 2, 3, 4, 5, 6, 7, 8, 9];
         let kmeans_algo = KMeansAlgorithm::new(
             indices,
             &embeddings,
             dim,
             2,
-            0,
+            1,
+            10,
             8,
-            1000,
             chroma_distance::DistanceFunction::Euclidean,
             100.0,
         );
@@ -456,18 +458,18 @@ mod tests {
         // 2D embeddings.
         let dim = 2;
         let embeddings = [
-            0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 5.0, 5.0, 10.0, 10.0, 11.0, 10.0, 10.0, 11.0,
-            11.0, 11.0,
+            -1.0, -1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 5.0, 5.0, 10.0, 10.0, 11.0, 10.0,
+            10.0, 11.0, 11.0, 11.0, 12.0, 12.0, 13.0, 13.0,
         ];
-        let indices: Vec<u32> = vec![0, 1, 2, 3, 4, 5, 6, 7, 8];
+        let indices: Vec<u32> = vec![0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11];
         let kmeans_algo = KMeansAlgorithm::new(
             indices,
             &embeddings,
             dim,
             2,
-            0,
+            1,
+            12,
             9,
-            1000,
             chroma_distance::DistanceFunction::Euclidean,
             100.0,
         );
@@ -479,7 +481,7 @@ mod tests {
         assert_eq!(res.cluster_counts, vec![5, 4]);
         assert_eq!(res.total_distance, 94.0);
         assert_eq!(res.cluster_farthest_distance, vec![50.0, 11.0]);
-        assert_eq!(res.cluster_farthest_point_idx, vec![4, 8]);
+        assert_eq!(res.cluster_farthest_point_idx, vec![5, 9]);
         assert_eq!(
             res.cluster_new_centers,
             vec![vec![7.0, 7.0], vec![42.0, 42.0]]
@@ -501,7 +503,7 @@ mod tests {
             2,
             0,
             8,
-            1000,
+            4,
             chroma_distance::DistanceFunction::Euclidean,
             100.0,
         );
@@ -526,4 +528,38 @@ mod tests {
         assert_eq!(res.cluster_nearest_point_idx, vec![0, 4]);
         assert_eq!(res.cluster_labels, labels);
     }
+
+    #[test]
+    fn test_kmeans_clustering() {
+        // 2D embeddings.
+        let dim = 2;
+        let embeddings = [
+            0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 10.0, 10.0, 11.0, 10.0, 10.0, 11.0, 11.0, 11.0,
+        ];
+        let indices: Vec<u32> = vec![0, 1, 2, 3, 4, 5, 6, 7];
+        let mut kmeans_algo = KMeansAlgorithm::new(
+            indices,
+            &embeddings,
+            dim,
+            2,
+            0,
+            8,
+            4,
+            chroma_distance::DistanceFunction::Euclidean,
+            100.0,
+        );
+        let res = kmeans_algo.cluster();
+        assert_eq!(res.cluster_counts, vec![4, 4]);
+        let mut labels = HashMap::new();
+        labels.insert(0, res.cluster_labels[&0]);
+        labels.insert(1, res.cluster_labels[&0]);
+        labels.insert(2, res.cluster_labels[&0]);
+        labels.insert(3, res.cluster_labels[&0]);
+        labels.insert(4, res.cluster_labels[&4]);
+        labels.insert(5, res.cluster_labels[&4]);
+        labels.insert(6, res.cluster_labels[&4]);
+        labels.insert(7, res.cluster_labels[&4]);
+        assert_eq!(res.cluster_labels, labels);
+        println!("{:?}", res);
+    }
 }