Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6880#discussion_r33736217
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/DpMeans.scala ---
@@ -0,0 +1,248 @@
+/*
+ * 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.spark.mllib.clustering
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * :: Experimental ::
+ *
+ * The Dirichlet process (DP) is a popular non-parametric Bayesian mixture
+ * model that allows for flexible clustering of data without having to
+ * determine the number of clusters in advance.
+ *
+ * Given a set of data points, this class performs cluster creation
process,
+ * based on DP means algorithm, iterating until the maximum number of
iterations
+ * is reached or the convergence criteria is satisfied. With the current
+ * global set of centers, it locally creates a new cluster centered at `x`
+ * whenever it encounters an uncovered data point `x`. In a similar manner,
+ * a local cluster center is promoted to a global center whenever an
uncovered
+ * local cluster center is found. A data point is said to be "covered" by
+ * a cluster `c` if the distance from the point to the cluster center of
`c`
+ * is less than a given lambda value.
+ *
+ * The original paper is "MLbase: Distributed Machine Learning Made Easy"
by
+ * Xinghao Pan, Evan R. Sparks, Andre Wibisono
--- End diff --
Should it be "Revisiting k-means: New Algorithms via Bayesian
Nonparametrics" instead?
http://machinelearning.wustl.edu/mlpapers/papers/ICML2012Kulis_291
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