diff --git a/src/main/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java b/src/main/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
index 2a1e7d1dd..2f63b63d3 100644
--- a/src/main/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
+++ b/src/main/java/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximization.java
@@ -31,7 +31,6 @@ import org.apache.commons.math3.linear.Array2DRowRealMatrix;
 import org.apache.commons.math3.linear.RealMatrix;
 import org.apache.commons.math3.linear.SingularMatrixException;
 import org.apache.commons.math3.stat.correlation.Covariance;
-import org.apache.commons.math3.util.FastMath;
 import org.apache.commons.math3.util.MathArrays;
 import org.apache.commons.math3.util.Pair;
 
@@ -323,28 +322,23 @@ public class MultivariateNormalMixtureExpectationMaximization {
         }
         Arrays.sort(sortedData);
 
-        final int totalBins = numComponents;
-
         // uniform weight for each bin
-        final double weight = 1d / totalBins;
+        final double weight = 1d / numComponents;
 
         // components of mixture model to be created
         final List<Pair<Double, MultivariateNormalDistribution>> components =
                 new ArrayList<Pair<Double, MultivariateNormalDistribution>>();
 
         // create a component based on data in each bin
-        for (int binNumber = 1; binNumber <= totalBins; binNumber++) {
-            // minimum index from sorted data for this bin
-            final int minIndex
-                = (int) FastMath.max(0,
-                                     FastMath.floor((binNumber - 1) * numRows / totalBins));
+        for (int binIndex = 0; binIndex < numComponents; binIndex++) {
+            // minimum index (inclusive) from sorted data for this bin
+            final int minIndex = (binIndex * numRows) / numComponents;
 
-            // maximum index from sorted data for this bin
-            final int maxIndex
-                = (int) FastMath.ceil(binNumber * numRows / numComponents) - 1;
+            // maximum index (exclusive) from sorted data for this bin
+            final int maxIndex = ((binIndex + 1) * numRows) / numComponents;
 
             // number of data records that will be in this bin
-            final int numBinRows = maxIndex - minIndex + 1;
+            final int numBinRows = maxIndex - minIndex;
 
             // data for this bin
             final double[][] binData = new double[numBinRows][numCols];
@@ -353,7 +347,7 @@ public class MultivariateNormalMixtureExpectationMaximization {
             final double[] columnMeans = new double[numCols];
 
             // populate bin and create component
-            for (int i = minIndex, iBin = 0; i <= maxIndex; i++, iBin++) {
+            for (int i = minIndex, iBin = 0; i < maxIndex; i++, iBin++) {
                 for (int j = 0; j < numCols; j++) {
                     final double val = sortedData[i].getRow()[j];
                     columnMeans[j] += val;
@@ -426,6 +420,27 @@ public class MultivariateNormalMixtureExpectationMaximization {
             return mean.compareTo(other.mean);
         }
 
+        /** {@inheritDoc} */
+        @Override
+        public boolean equals(Object other) {
+
+            if (this == other) {
+                return true;
+            }
+
+            if (other instanceof DataRow) {
+                return MathArrays.equals(row, ((DataRow) other).row);
+            }
+
+            return false;
+
+        }
+
+        /** {@inheritDoc} */
+        @Override
+        public int hashCode() {
+            return Arrays.hashCode(row);
+        }
         /**
          * Get a data row.
          * @return data row array