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diff --git a/src/main/java/org/apache/commons/math3/ml/clustering/evaluation/ClusterEvaluator.java b/src/main/java/org/apache/commons/math3/ml/clustering/evaluation/ClusterEvaluator.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.CentroidCluster;
+import org.apache.commons.math3.ml.clustering.Cluster;
+import org.apache.commons.math3.ml.clustering.Clusterable;
+import org.apache.commons.math3.ml.clustering.DoublePoint;
+import org.apache.commons.math3.ml.distance.DistanceMeasure;
+import org.apache.commons.math3.ml.distance.EuclideanDistance;
+
+/**
+ * Base class for cluster evaluation methods.
+ *
+ * @param <T> type of the clustered points
+ * @since 3.3
+ */
+public abstract class ClusterEvaluator<T extends Clusterable> {
+
+    /** The distance measure to use when evaluating the cluster. */
+    private final DistanceMeasure measure;
+
+    /**
+     * Creates a new cluster evaluator with an {@link EuclideanDistance}
+     * as distance measure.
+     */
+    public ClusterEvaluator() {
+        this(new EuclideanDistance());
+    }
+
+    /**
+     * Creates a new cluster evaluator with the given distance measure.
+     * @param measure the distance measure to use
+     */
+    public ClusterEvaluator(final DistanceMeasure measure) {
+        this.measure = measure;
+    }
+
+    /**
+     * Computes the evaluation score for the given list of clusters.
+     * @param clusters the clusters to evaluate
+     * @return the computed score
+     */
+    public abstract double score(List<? extends Cluster<T>> clusters);
+
+    /**
+     * Returns whether the first evaluation score is considered to be better
+     * than the second one by this evaluator.
+     * <p>
+     * Specific implementations shall override this method if the returned scores
+     * do not follow the same ordering, i.e. smaller score is better.
+     *
+     * @param score1 the first score
+     * @param score2 the second score
+     * @return {@code true} if the first score is considered to be better, {@code false} otherwise
+     */
+    public boolean isBetterScore(double score1, double score2) {
+        return score1 < score2;
+    }
+
+    /**
+     * Calculates the distance between two {@link Clusterable} instances
+     * with the configured {@link DistanceMeasure}.
+     *
+     * @param p1 the first clusterable
+     * @param p2 the second clusterable
+     * @return the distance between the two clusterables
+     */
+    protected double distance(final Clusterable p1, final Clusterable p2) {
+        return measure.compute(p1.getPoint(), p2.getPoint());
+    }
+
+    /**
+     * Computes the centroid for a cluster.
+     *
+     * @param cluster the cluster
+     * @return the computed centroid for the cluster,
+     * or {@code null} if the cluster does not contain any points
+     */
+    protected Clusterable centroidOf(final Cluster<T> cluster) {
+        final List<T> points = cluster.getPoints();
+        if (points.isEmpty()) {
+            return null;
+        }
+
+        // in case the cluster is of type CentroidCluster, no need to compute the centroid
+        if (cluster instanceof CentroidCluster) {
+            return ((CentroidCluster<T>) cluster).getCenter();
+        }
+
+        final int dimension = points.get(0).getPoint().length;
+        final double[] centroid = new double[dimension];
+        for (final T p : points) {
+            final double[] point = p.getPoint();
+            for (int i = 0; i < centroid.length; i++) {
+                centroid[i] += point[i];
+            }
+        }
+        for (int i = 0; i < centroid.length; i++) {
+            centroid[i] /= points.size();
+        }
+        return new DoublePoint(centroid);
+    }
+
+}