Javadoc (errors and warnings).

src/main/java/org/apache/commons/math4/ml/clustering/MiniBatchKMeansClusterer.java

@@ -63,7 +63,7 @@ public class MiniBatchKMeansClusterer<T extends Clusterable> extends KMeansPlusP
      *
      * @param k                     the number of clusters to split the data into
      * @param maxIterations         the maximum number of iterations to run the algorithm for all the points,
-     *                              for mini batch actual iterations <= maxIterations * points.size() / batchSize
+     *                              for mini batch actual {@code iterations <= maxIterations * points.size() / batchSize}.
      *                              If negative, no maximum will be used.
      * @param batchSize             the mini batch size for training iterations.
      * @param initIterations        the iterations to find out the best clusters centers with mini batch.
@@ -74,6 +74,8 @@ public class MiniBatchKMeansClusterer<T extends Clusterable> extends KMeansPlusP
      * @param measure               the distance measure to use, EuclideanDistance is recommended.
      * @param random                random generator to use for choosing initial centers
      *                              may appear during algorithm iterations
+     * @param emptyStrategy Strategy to use for handling empty clusters that
+     * may appear during algorithm iterations.
      */
     public MiniBatchKMeansClusterer(final int k, final int maxIterations, final int batchSize, final int initIterations,
                                     final int initBatchSize, final int maxNoImprovementTimes,

src/main/java/org/apache/commons/math4/ml/clustering/evaluation/SumOfClusterVariances.java

@@ -46,8 +46,7 @@ public class SumOfClusterVariances implements ClusterEvaluator {
         this.measure = measure;
     }
 
-    /** {@inheritDoc}
-     * @param clusters*/
+    /** {@inheritDoc} */
     public double score(List<? extends Cluster<? extends Clusterable>> clusters) {
         double varianceSum = 0.0;
         for (final Cluster<? extends Clusterable> cluster : clusters) {

src/main/java/org/apache/commons/math4/ml/clustering/initialization/CentroidInitializer.java

@@ -31,6 +31,7 @@ public interface CentroidInitializer {
     /**
      * Choose the initial centers.
      *
+     * @param <T> Type of points to cluster.
      * @param points the points to choose the initial centers from
      * @param k      The number of clusters
      * @return the initial centers