diff --git a/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanModel.java b/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanModel.java
index 03558ffd3..c3336da3a 100644
--- a/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanModel.java
+++ b/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanModel.java
@@ -19,6 +19,7 @@
 import com.oracle.labs.mlrg.olcut.util.Pair;
 import org.tribuo.Example;
 import org.tribuo.Excuse;
+import org.tribuo.Feature;
 import org.tribuo.ImmutableFeatureMap;
 import org.tribuo.ImmutableOutputInfo;
 import org.tribuo.Model;
@@ -29,6 +30,7 @@
 import org.tribuo.math.la.DenseVector;
 import org.tribuo.math.la.SGDVector;
 import org.tribuo.math.la.SparseVector;
+import org.tribuo.math.la.VectorTuple;
 import org.tribuo.provenance.ModelProvenance;
 
 import java.io.IOException;
@@ -107,6 +109,43 @@ public List<Double> getOutlierScores() {
         return outlierScores;
     }
 
+    /**
+     * Returns a deep copy of the cluster exemplars.
+     * @return The cluster exemplars.
+     */
+    public List<HdbscanTrainer.ClusterExemplar> getClusterExemplars() {
+        List<HdbscanTrainer.ClusterExemplar> list = new ArrayList<>(clusterExemplars.size());
+        for (HdbscanTrainer.ClusterExemplar e : clusterExemplars) {
+            list.add(e.copy());
+        }
+        return list;
+    }
+
+    /**
+     * Returns the features in each cluster exemplar.
+     * <p>
+     * In many cases this should be used in preference to {@link #getClusterExemplars()}
+     * as it performs the mapping from Tribuo's internal feature ids to
+     * the externally visible feature names.
+     * @return The cluster exemplars.
+     */
+    public List<Pair<Integer,List<Feature>>> getClusters() {
+        List<Pair<Integer,List<Feature>>> list = new ArrayList<>(clusterExemplars.size());
+
+        for (HdbscanTrainer.ClusterExemplar e : clusterExemplars) {
+            List<Feature> features = new ArrayList<>(e.getFeatures().numActiveElements());
+
+            for (VectorTuple v : e.getFeatures()) {
+                Feature f = new Feature(featureIDMap.get(v.index).getName(),v.value);
+                features.add(f);
+            }
+
+            list.add(new Pair<>(e.getLabel(),features));
+        }
+
+        return list;
+    }
+
     @Override
     public Prediction<ClusterID> predict(Example<ClusterID> example) {
         SGDVector vector;
diff --git a/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanTrainer.java b/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanTrainer.java
index 6c0992c84..2e8e9153e 100644
--- a/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanTrainer.java
+++ b/Clustering/Hdbscan/src/main/java/org/tribuo/clustering/hdbscan/HdbscanTrainer.java
@@ -50,6 +50,7 @@
 import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;
+import java.util.Objects;
 import java.util.PriorityQueue;
 import java.util.TreeMap;
 import java.util.TreeSet;
@@ -805,7 +806,7 @@ public TrainerProvenance getProvenance() {
     /**
      * A cluster exemplar, with attributes for the point's label, outlier score and its features.
      */
-    final static class ClusterExemplar implements Serializable {
+    public final static class ClusterExemplar implements Serializable {
         private static final long serialVersionUID = 1L;
 
         private final Integer label;
@@ -820,19 +821,38 @@ final static class ClusterExemplar implements Serializable {
             this.maxDistToEdge = maxDistToEdge;
         }
 
-        Integer getLabel() {
+        /**
+         * Get the label in this exemplar.
+         * @return The label.
+         */
+        public Integer getLabel() {
             return label;
         }
 
-        Double getOutlierScore() {
+        /**
+         * Get the outlier score in this exemplar.
+         * @return The outlier score.
+         */
+        public Double getOutlierScore() {
             return outlierScore;
         }
 
-        SGDVector getFeatures() {
+        /**
+         * Get the feature vector in this exemplar.
+         * @return The feature vector.
+         */
+        public SGDVector getFeatures() {
             return features;
         }
 
-        Double getMaxDistToEdge() {
+        /**
+         * Get the maximum distance from this exemplar to the edge of the cluster.
+         * <p>
+         * For models trained in 4.2 this will return {@link Double#NEGATIVE_INFINITY} as that information is 
+         * not produced by 4.2 models.
+         * @return The distance to the edge of the cluster.
+         */
+        public Double getMaxDistToEdge() {
             if (maxDistToEdge != null) {
                 return maxDistToEdge;
             }
@@ -840,6 +860,33 @@ Double getMaxDistToEdge() {
                 return Double.NEGATIVE_INFINITY;
             }
         }
+
+        /**
+         * Copies this cluster exemplar.
+         * @return A deep copy of this cluster exemplar.
+         */
+        public ClusterExemplar copy() {
+            return new ClusterExemplar(label,outlierScore,features.copy(),maxDistToEdge);
+        }
+
+        @Override
+        public String toString() {
+            double dist = maxDistToEdge == null ? Double.NEGATIVE_INFINITY : maxDistToEdge;
+            return "ClusterExemplar(label="+label+",outlierScore="+outlierScore+",vector="+features+",maxDistToEdge="+dist+")";
+        }
+
+        @Override
+        public boolean equals(Object o) {
+            if (this == o) return true;
+            if (o == null || getClass() != o.getClass()) return false;
+            ClusterExemplar that = (ClusterExemplar) o;
+            return label.equals(that.label) && outlierScore.equals(that.outlierScore) && features.equals(that.features) && Objects.equals(maxDistToEdge, that.maxDistToEdge);
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(label, outlierScore, features, maxDistToEdge);
+        }
     }
     
 }
diff --git a/Clustering/Hdbscan/src/test/java/org/tribuo/clustering/hdbscan/TestHdbscan.java b/Clustering/Hdbscan/src/test/java/org/tribuo/clustering/hdbscan/TestHdbscan.java
index 8474e548f..925012a1b 100644
--- a/Clustering/Hdbscan/src/test/java/org/tribuo/clustering/hdbscan/TestHdbscan.java
+++ b/Clustering/Hdbscan/src/test/java/org/tribuo/clustering/hdbscan/TestHdbscan.java
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2021, 2022 Oracle and/or its affiliates. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -21,6 +21,7 @@
 import org.junit.jupiter.api.Test;
 import org.tribuo.DataSource;
 import org.tribuo.Dataset;
+import org.tribuo.Feature;
 import org.tribuo.Model;
 import org.tribuo.MutableDataset;
 import org.tribuo.Prediction;
@@ -37,7 +38,10 @@
 import org.tribuo.data.columnar.processors.response.EmptyResponseProcessor;
 import org.tribuo.data.csv.CSVDataSource;
 import org.tribuo.evaluation.TrainTestSplitter;
+import org.tribuo.impl.ArrayExample;
 import org.tribuo.math.distance.DistanceType;
+import org.tribuo.math.la.DenseVector;
+import org.tribuo.math.la.SGDVector;
 import org.tribuo.test.Helpers;
 
 import java.io.FileInputStream;
@@ -47,8 +51,10 @@
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.HashMap;
+import java.util.HashSet;
 import java.util.List;
 import java.util.Map;
+import java.util.Set;
 import java.util.logging.Level;
 import java.util.logging.Logger;
 import java.util.stream.Collectors;
@@ -57,6 +63,7 @@
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
 import static org.junit.jupiter.api.Assertions.fail;
 
 /**
@@ -205,8 +212,35 @@ public static void runBasicTrainPredict(HdbscanTrainer trainer) {
         Dataset<ClusterID> testData = new MutableDataset<>(splitter.getTest());
 
         HdbscanModel model = trainer.train(trainData);
+
+        for (HdbscanTrainer.ClusterExemplar e : model.getClusterExemplars()) {
+            assertTrue(e.getMaxDistToEdge() > 0.0);
+        }
+
         List<Integer> clusterLabels = model.getClusterLabels();
         List<Double> outlierScores = model.getOutlierScores();
+        List<Pair<Integer,List<Feature>>> exemplarLists = model.getClusters();
+        List<HdbscanTrainer.ClusterExemplar> exemplars = model.getClusterExemplars();
+
+        assertEquals(exemplars.size(), exemplarLists.size());
+
+        // Check there's at least one exemplar per label
+        Set<Integer> exemplarLabels = exemplarLists.stream().map(Pair::getA).collect(Collectors.toSet());
+        Set<Integer> clusterLabelSet = new HashSet<>(clusterLabels);
+        // Remove the noise label
+        clusterLabelSet.remove(Integer.valueOf(0));
+        assertEquals(exemplarLabels,clusterLabelSet);
+
+        for (int i = 0; i < exemplars.size(); i++) {
+            HdbscanTrainer.ClusterExemplar e = exemplars.get(i);
+            Pair<Integer, List<Feature>> p = exemplarLists.get(i);
+            assertEquals(model.getFeatureIDMap().size(), e.getFeatures().size());
+            assertEquals(p.getB().size(), e.getFeatures().size());
+            SGDVector otherFeatures = DenseVector.createDenseVector(
+                    new ArrayExample<>(trainData.getOutputFactory().getUnknownOutput(), p.getB()),
+                    model.getFeatureIDMap(), false);
+            assertEquals(otherFeatures, e.getFeatures());
+        }
 
         int [] expectedIntClusterLabels = {4,3,4,5,3,5,3,4,3,4,5,5,3,4,4,0,3,4,0,5,5,3,3,4,4,4,4,4,4,4,4,4,4,0,4,5,3,5,3,4,3,4,4,3,0,5,0,4,4,4,4,4,5,4,3,4,4,4,4,4,5,3,4,3,5,3,4,5,3,4,0,5,4,4,4,4,4,5,4,4,4,4,4,5,3,4,4,3,4,3,5,5,0,5,4,4,3,5,5,4,5,5,3,5,4,4,3,5,4,5,5,5,4,4,5,5,3,5,4,4,3,5,5,3,5,4,4,5,5,5,3,5,4,5,3,4,3,5,4,4,3,3,5,4,4,5,5,4,3,4,5,4,5,4,3,3,3,4,5,4,5,5,3,4,3,3,4,5,3,5,5,5,5,5,4,4,3,4,5,5,4,4,3,4,3,4,5,4,4,5,4,3,3,0,3,5,5,3,3,3,4,3,3,5,5,5,5,3,5,5,3,5,3,4,5,3,3,3,4,4,3,3,3,5,3,4,5,3,5,5,5,3,5,3,5,4,5,4,4,5,5,5,3,5,4,5,5,4,4,4,5,4,5,4,3,3,4,5,4,4,3,3,3,4,5,4,4,4,4,5,4,4,4,5,3,5,4,5,3,5,3,5,4,4,0,4,4,5,3,4,5,5,0,5,4,5,3,4,3,5,5,4,5,5,5,5,5,5,3,5,4,3,3,5,3,4,5,4,3,5,4,3,3,3,5,4,5,4,5,5,4,3,5,4,5,4,5,4,3,4,5,4,4,5,5,5,3,4,5,4,0,3,5,3,4,3,3,5,5,5,4,4,3,3,4,3,5,3,3,4,3,5,3,4,5,4,4,3,4,4,3,3,5,4,4,5,3,5,3,3,4,5,3,4,5,5,4,4,4,5,5,5,5,3,3,4,4,4,4,4,3,5,4,3,4,4,5,3,5,3,4,5,4,4,5,3,4,4,4,5,5,4,5,0,4,5,3,4,5,4,4,4,5,4,4,4,0,3,4,5,5,4,4,3,3,4,3,3,4,5,5,4,3,5,4,4,4,4,5,4,4,3,4,5,5,4,3,4,5,4,3,5,5,5,3,4,4,4,4,4,4,5,3,3,3,5,5,4,5,3,5,3,5,4,5,3,4,5,4,3,5,4,4,5,5,0,3,3,5,5,3,0,5,5,5,5,3,4,5,4,3,3,4,5,4,4,0,5,3,4,4,4,4,5,5,5,3,5,4,3,3,5,3,4,3,5,3,3,4,3,5,4,3,4,3,0,4,5,5,5,3,4,3,5,5,4,5,4,4,4,5,4,3,4,3,4,5,3,5,4,5,3,0,4,0,4,3,3,4,3,0,3,3,3,3,4,4,5,3,3,5,4,4,4,5,5,5,3,3,4,4,3,4,5,3,4,4,5,3,4,4,4,3,4,4,4,5,4,4,5,5,5,4,4,4,5,5,5,5,4,3,4,3,3,3,4,4,5,4,5,4,4,4,4,4,5,4,5,5,5,4,3,5,3,5,4,5,4,4,5,0,5,3,4,5,4,4,5,3,4,4,3,5,4,4,4,5,3,3,4,4,5,5,5,3,4,3,4,5,5,4,4,3,3,4,4,5,5,5,3,4,3,4,4,4,5,5,5,0,4,5,5,3,3,4,5,4,3,3,4,3,4,5,4,3,4,5,5,3,3,4,4,3,3,5,4,5,3,4,5,4,3,3,4,5,5,5,3,3,4,4,5,5,5,4,5,5,5,4,4,4,5,4,5,5,3,3,4,4,3,5,5,3,3,4,4,5,3,3,3};
         List<Integer> expectedClusterLabels = Arrays.stream(expectedIntClusterLabels).boxed().collect(Collectors.toList());
@@ -258,6 +292,11 @@ public void deserializeHdbscanModelV42Test() {
             fail("There is a problem deserializing the model file " + serializedModelPath);
         }
 
+        // In v4.2 models this value is unset and defaults to negative infinity.
+        for (HdbscanTrainer.ClusterExemplar e : model.getClusterExemplars()) {
+            assertEquals(Double.NEGATIVE_INFINITY, e.getMaxDistToEdge());
+        }
+
         ClusteringFactory clusteringFactory = new ClusteringFactory();
         ResponseProcessor<ClusterID> emptyResponseProcessor = new EmptyResponseProcessor<>(clusteringFactory);
         Map<String, FieldProcessor> regexMappingProcessors = new HashMap<>();