diff --git a/src/changes/changes.xml b/src/changes/changes.xml
index 5fa4572de..df71204e5 100644
--- a/src/changes/changes.xml
+++ b/src/changes/changes.xml
@@ -73,6 +73,10 @@ Users are encouraged to upgrade to this version as this release not
   2. A few methods in the FastMath class are in fact slower that their
   counterpart in either Math or StrictMath (cf. MATH-740 and MATH-901).
 ">
+      <action dev="tn" type="fix" issue="MATH-1165" due-to="Pashutan Modaresi">
+        "FuzzyKMeansClusterer" has thrown an exception in case one of the data
+        points was equal to a cluster center.
+      </action>
       <action dev="erans" type="add" issue="MATH-1144">
         Interface to allow parameter validation in "o.a.c.m.fitting.leastsquares":
         the point computed by by the optimizer can be modified before evaluation.
diff --git a/src/main/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClusterer.java b/src/main/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClusterer.java
index ed2204789..5f89934a7 100644
--- a/src/main/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClusterer.java
+++ b/src/main/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClusterer.java
@@ -346,18 +346,32 @@ public class FuzzyKMeansClusterer<T extends Clusterable> extends Clusterer<T> {
     private void updateMembershipMatrix() {
         for (int i = 0; i < points.size(); i++) {
             final T point = points.get(i);
-            double maxMembership = 0.0;
+            double maxMembership = Double.MIN_VALUE;
             int newCluster = -1;
             for (int j = 0; j < clusters.size(); j++) {
                 double sum = 0.0;
                 final double distA = FastMath.abs(distance(point, clusters.get(j).getCenter()));
 
-                for (final CentroidCluster<T> c : clusters) {
-                    final double distB = FastMath.abs(distance(point, c.getCenter()));
-                    sum += FastMath.pow(distA / distB, 2.0 / (fuzziness - 1.0));
+                if (distA != 0.0) {
+                    for (final CentroidCluster<T> c : clusters) {
+                        final double distB = FastMath.abs(distance(point, c.getCenter()));
+                        if (distB == 0.0) {
+                            sum = Double.POSITIVE_INFINITY;
+                            break;
+                        }
+                        sum += FastMath.pow(distA / distB, 2.0 / (fuzziness - 1.0));
+                    }
                 }
 
-                membershipMatrix[i][j] = 1.0 / sum;
+                double membership;
+                if (sum == 0.0) {
+                    membership = 1.0;
+                } else if (sum == Double.POSITIVE_INFINITY) {
+                    membership = 0.0;
+                } else {
+                    membership = 1.0 / sum;
+                }
+                membershipMatrix[i][j] = membership;
 
                 if (membershipMatrix[i][j] > maxMembership) {
                     maxMembership = membershipMatrix[i][j];
diff --git a/src/test/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClustererTest.java b/src/test/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClustererTest.java
index a4a6b8c1f..885b9c25d 100644
--- a/src/test/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClustererTest.java
+++ b/src/test/java/org/apache/commons/math3/ml/clustering/FuzzyKMeansClustererTest.java
@@ -16,9 +16,6 @@
  */
 package org.apache.commons.math3.ml.clustering;
 
-import org.hamcrest.CoreMatchers;
-import org.junit.Assert;
-
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
@@ -29,6 +26,8 @@ import org.apache.commons.math3.ml.distance.CanberraDistance;
 import org.apache.commons.math3.ml.distance.DistanceMeasure;
 import org.apache.commons.math3.random.JDKRandomGenerator;
 import org.apache.commons.math3.random.RandomGenerator;
+import org.hamcrest.CoreMatchers;
+import org.junit.Assert;
 import org.junit.Test;
 
 /**
@@ -106,4 +105,31 @@ public class FuzzyKMeansClustererTest {
         Assert.assertThat(clusterer.getRandomGenerator(), CoreMatchers.is(random));
     }
 
+    @Test
+    public void testSingleCluster() {
+        final List<DoublePoint> points = new ArrayList<DoublePoint>();
+        points.add(new DoublePoint(new double[] { 1, 1 }));
+
+        final FuzzyKMeansClusterer<DoublePoint> transformer =
+                new FuzzyKMeansClusterer<DoublePoint>(1, 2.0);
+        final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
+
+        Assert.assertEquals(1, clusters.size());
+    }
+
+    @Test
+    public void testClusterCenterEqualsPoints() {
+        final List<DoublePoint> points = new ArrayList<DoublePoint>();
+        points.add(new DoublePoint(new double[] { 1, 1 }));
+        points.add(new DoublePoint(new double[] { 1.00001, 1.00001 }));
+        points.add(new DoublePoint(new double[] { 2, 2 }));
+        points.add(new DoublePoint(new double[] { 3, 3 }));
+
+        final FuzzyKMeansClusterer<DoublePoint> transformer =
+                new FuzzyKMeansClusterer<DoublePoint>(3, 2.0);
+        final List<CentroidCluster<DoublePoint>> clusters = transformer.cluster(points);
+
+        Assert.assertEquals(3, clusters.size());
+    }
+
 }