diff --git a/elki-clustering/src/main/java/elki/clustering/kmeans/BetulaLloydKMeans.java b/elki-clustering/src/main/java/elki/clustering/kmeans/BetulaLloydKMeans.java
index 919f99081b..6028a5e6a6 100644
--- a/elki-clustering/src/main/java/elki/clustering/kmeans/BetulaLloydKMeans.java
+++ b/elki-clustering/src/main/java/elki/clustering/kmeans/BetulaLloydKMeans.java
@@ -200,6 +200,7 @@ else if(storeIds) {
   private double[][] kmeans(ArrayList<? extends ClusterFeature> cfs, int[] assignment, int[] weights, CFTree<?> tree) {
     double[][] means = initialization.chooseInitialMeans(tree, cfs, k);
     for(int i = 1; i <= maxiter || maxiter < 0; i++) {
+      Duration duration = LOG.newDuration(getClass().getName() + "." + i + ".time").begin();
       long prevdiststat = diststat;
       means = i == 1 ? means : means(assignment, means, cfs, weights);
       if(i > 1 && LOG.isStatistics()) {
@@ -209,6 +210,7 @@ private double[][] kmeans(ArrayList<? extends ClusterFeature> cfs, int[] assignm
       }
       int changed = assignToNearestCluster(assignment, means, cfs, weights);
       if(LOG.isStatistics()) {
+        LOG.statistics(duration.end());
         LOG.statistics(new LongStatistic(getClass().getName() + "." + i + ".reassigned", changed));
         if(diststat > prevdiststat) {
           LOG.statistics(new LongStatistic(getClass().getName() + "." + i + ".distance-computations", diststat - prevdiststat));
@@ -237,7 +239,7 @@ private double[][] means(int[] assignment, double[][] means, ArrayList<? extends
       int c = assignment[i];
       final ClusterFeature cf = cfs.get(i);
       int d = cf.getDimensionality();
-      int n = cf.getWeight();
+      int n = ignoreWeight ? 1 : cf.getWeight();
       if(newMeans[c] == null) {
         newMeans[c] = new double[d];
         for(int j = 0; j < d; j++) {
@@ -292,7 +294,7 @@ private int assignToNearestCluster(int[] assignment, double[][] means, ArrayList
         changed++;
         assignment[i] = minIndex;
       }
-      weights[minIndex] += ignoreWeight ? 1 : cfsi.getWeight();
+      weights[minIndex] += cfsi.getWeight();
     }
     return changed;
   }
diff --git a/elki-clustering/src/test/java/elki/clustering/kmeans/BetulaLloydKMeansTest.java b/elki-clustering/src/test/java/elki/clustering/kmeans/BetulaLloydKMeansTest.java
index 19e8e18a6e..703ac3d44a 100644
--- a/elki-clustering/src/test/java/elki/clustering/kmeans/BetulaLloydKMeansTest.java
+++ b/elki-clustering/src/test/java/elki/clustering/kmeans/BetulaLloydKMeansTest.java
@@ -78,8 +78,8 @@ public void testNaive() {
         .with(AbstractKMeans.K_ID, 4) //
         .with(AbstractCFKMeansInitialization.Par.SEED_ID, 0) //
         .build().autorun(db);
-    assertFMeasure(db, clustering, 0.86656);
-    assertClusterSizes(clustering, new int[] { 85, 127, 203, 223 });
+    assertFMeasure(db, clustering, 0.88199);
+    assertClusterSizes(clustering, new int[] { 76, 141, 202, 219 });
   }
 
   @Test