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diff --git a/src/main/java/org/apache/commons/math3/ml/clustering/evaluation/SumOfClusterVariances.java b/src/main/java/org/apache/commons/math3/ml/clustering/evaluation/SumOfClusterVariances.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math3.ml.clustering.evaluation;
+
+import java.util.List;
+
+import org.apache.commons.math3.ml.clustering.Cluster;
+import org.apache.commons.math3.ml.clustering.Clusterable;
+import org.apache.commons.math3.ml.distance.DistanceMeasure;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+
+/**
+ * Computes the sum of intra-cluster distance variances according to the formula:
+ * <pre>
+ * \( score = \sum\limits_{i=1}^n \sigma_i^2 \)
+ * </pre>
+ * where n is the number of clusters and \( \sigma_i^2 \) is the variance of
+ * intra-cluster distances of cluster \( c_i \).
+ *
+ * @param <T> the type of the clustered points
+ * @since 3.3
+ */
+public class SumOfClusterVariances<T extends Clusterable> extends ClusterEvaluator<T> {
+
+    /**
+     *
+     * @param measure the distance measure to use
+     */
+    public SumOfClusterVariances(final DistanceMeasure measure) {
+        super(measure);
+    }
+
+    /** {@inheritDoc} */
+    @Override
+    public double score(final List<? extends Cluster<T>> clusters) {
+        double varianceSum = 0.0;
+        for (final Cluster<T> cluster : clusters) {
+            if (!cluster.getPoints().isEmpty()) {
+
+                final Clusterable center = centroidOf(cluster);
+
+                // compute the distance variance of the current cluster
+                final Variance stat = new Variance();
+                for (final T point : cluster.getPoints()) {
+                    stat.increment(distance(point, center));
+                }
+                varianceSum += stat.getResult();
+
+            }
+        }
+        return varianceSum;
+    }
+
+}