diff --git a/data/mllib/pic_data.txt b/data/mllib/pic_data.txt
index e69de29bb2d1d..0134c91791cc5 100644
--- a/data/mllib/pic_data.txt
+++ b/data/mllib/pic_data.txt
@@ -0,0 +1,299 @@
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+
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+
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+1000	0.031638	0.000526	0.000110	0.018844	0.001752	0.027751	0.000452	0.033239	0.007962	0.001933	0.013124	0.036602	0.004916	0.000641	0.008054	0.008521	0.000915	0.025550	0.007614	0.012574	0.017354	0.000093	0.033650	0.000089	0.002070	0.000917	0.011188	0.012137	0.016133	0.003681	0.002322	0.003205	0.026156	0.000389	0.000449	0.004834	0.000166	0.018126	0.000105	0.015739	0.012613	0.036466	0.025486	0.000338	0.025060	0.001968	0.003266	0.022727	0.000508	0.009188	0.031647	0.014439	0.007636	0.000093	0.000260	0.002002	0.000086	0.002019	0.030504	0.000141	0.000437	0.000902	0.017064	0.012600	0.000518	0.001456	0.015578	0.024788	0.000656	0.033090	0.018428	0.000587	0.008164	0.020859	0.016111	0.002772	0.000182	0.011523	0.018341	0.032931	0.001362	0.005099	0.001321	0.000795	0.002767	0.009881	0.008831	0.004924	0.000228	0.012537	0.000000	0.012576	0.000129	0.005721	0.003198	0.031593	0.005168	0.008586	0.009071	0.001253
+
+
+1000	0.003544	0.008751	0.003474	0.025289	0.016222	0.003555	0.008489	0.003607	0.025228	0.016201	0.026470	0.008766	0.003767	0.008203	0.008849	0.025537	0.008317	0.016386	0.003803	0.008384	0.003453	0.003667	0.003579	0.003597	0.008311	0.008906	0.003750	0.026483	0.015875	0.003403	0.003391	0.008227	0.003691	0.003450	0.008620	0.016586	0.003704	0.025422	0.003654	0.003753	0.025558	0.008418	0.015741	0.003792	0.008912	0.003767	0.009011	0.003520	0.003800	0.025144	0.008727	0.025059	0.025319	0.003498	0.003425	0.016065	0.003600	0.008901	0.003635	0.003499	0.008632	0.003364	0.025268	0.009095	0.008375	0.003413	0.025484	0.008350	0.008730	0.015879	0.008882	0.003405	0.008300	0.003695	0.016407	0.015593	0.003424	0.025107	0.008400	0.016142	0.008386	0.008172	0.008888	0.003829	0.016282	0.025439	0.015521	0.015669	0.003714	0.003436	0.008684	0.000000	0.003713	0.003402	0.003830	0.008508	0.009024	0.003385	0.016596	0.003822
+
+
+1000	0.000022	0.046576	0.010331	0.004005	0.025051	0.000020	0.022000	0.000039	0.009927	0.032376	0.006803	0.000259	0.001844	0.007940	0.004379	0.012427	0.004381	0.001965	0.000993	0.000182	0.000019	0.044141	0.000028	0.025117	0.001278	0.040767	0.000523	0.007510	0.000813	0.000059	0.000095	0.000706	0.000095	0.001000	0.040808	0.019452	0.050959	0.004890	0.051097	0.000288	0.008879	0.000132	0.000601	0.034633	0.000709	0.006015	0.013631	0.000019	0.024956	0.007472	0.000402	0.004229	0.012024	0.016634	0.001837	0.014295	0.034691	0.021926	0.000058	0.006088	0.031887	0.000288	0.003920	0.002413	0.013780	0.000165	0.006606	0.000119	0.046676	0.000653	0.001248	0.000530	0.000260	0.000160	0.004380	0.006632	0.003410	0.005429	0.000140	0.000995	0.002387	0.000405	0.031669	0.016620	0.029717	0.011534	0.001360	0.002854	0.043964	0.000022	0.000180	0.007501	0.000000	0.000039	0.003390	0.000122	0.008026	0.000028	0.009778	0.010316
+
+
+1000	0.010289	0.000255	0.000980	0.009394	0.001953	0.016705	0.001260	0.003384	0.006953	0.000986	0.007696	0.004443	0.000057	0.004353	0.000246	0.004201	0.008083	0.002912	0.000094	0.046407	0.034607	0.000096	0.006006	0.000290	0.022054	0.000137	0.000160	0.007272	0.031801	0.051072	0.044128	0.031610	0.000987	0.010427	0.000406	0.000610	0.000028	0.006950	0.000058	0.000287	0.004134	0.013830	0.025034	0.000019	0.001274	0.000027	0.000137	0.024958	0.000018	0.009339	0.002348	0.012848	0.005214	0.000522	0.006103	0.004468	0.000161	0.000117	0.001830	0.001861	0.000695	0.024919	0.011600	0.000415	0.002346	0.034618	0.005058	0.031965	0.000171	0.014036	0.000702	0.016743	0.046500	0.000523	0.001346	0.010012	0.003359	0.011734	0.040761	0.006666	0.013905	0.040939	0.000118	0.000019	0.000640	0.003694	0.029635	0.019860	0.000022	0.044126	0.007997	0.006886	0.000039	0.000000	0.000039	0.021953	0.000177	0.051091	0.000793	0.000022
+
+
+1000	0.000990	0.004395	0.000093	0.007431	0.003021	0.000535	0.000697	0.003384	0.004893	0.006714	0.007119	0.008017	0.051025	0.000245	0.046791	0.008918	0.000176	0.019507	0.044086	0.000262	0.000162	0.000997	0.001856	0.000291	0.000123	0.013811	0.034587	0.007074	0.001037	0.000028	0.000022	0.000120	0.010339	0.000023	0.002317	0.024856	0.006026	0.009923	0.001855	0.025003	0.012661	0.002314	0.001943	0.016621	0.021980	0.050886	0.040947	0.000293	0.024942	0.004038	0.013757	0.004223	0.006414	0.000159	0.000028	0.001404	0.000528	0.031649	0.006050	0.000059	0.001289	0.000018	0.005470	0.040752	0.000401	0.000019	0.012215	0.000686	0.008025	0.004496	0.031502	0.000019	0.000179	0.016618	0.029678	0.000766	0.000039	0.003844	0.000415	0.009970	0.000141	0.000135	0.021905	0.034636	0.014388	0.011520	0.000652	0.000611	0.010278	0.000095	0.004457	0.007729	0.003386	0.000039	0.000000	0.001278	0.046459	0.000058	0.032200	0.043986
+
+
+1000	0.031667	0.000432	0.000228	0.018452	0.001974	0.033801	0.000660	0.022639	0.008879	0.001690	0.013205	0.025086	0.001427	0.001291	0.003149	0.007883	0.002057	0.016066	0.002324	0.025130	0.030602	0.000093	0.027672	0.000130	0.005230	0.000527	0.003676	0.012236	0.025496	0.011193	0.007607	0.008129	0.012577	0.001268	0.000450	0.002814	0.000094	0.016216	0.000085	0.005739	0.010451	0.036623	0.032933	0.000139	0.012520	0.000571	0.001337	0.033401	0.000185	0.010883	0.018238	0.016860	0.007821	0.000164	0.000791	0.002870	0.000105	0.000893	0.017312	0.000339	0.000528	0.003146	0.018408	0.005198	0.000898	0.004980	0.013051	0.036507	0.000444	0.032866	0.008196	0.002009	0.018335	0.008623	0.008860	0.004777	0.000499	0.013864	0.031732	0.025526	0.003299	0.012458	0.000656	0.000261	0.001903	0.008420	0.016036	0.009141	0.000109	0.026156	0.031622	0.012332	0.000088	0.015721	0.000918	0.000000	0.002036	0.020832	0.004872	0.000382
+
+
+1000	0.001235	0.008148	0.000377	0.011965	0.006688	0.000787	0.002031	0.003138	0.009557	0.012113	0.012236	0.008146	0.030581	0.000875	0.036612	0.015015	0.000659	0.020672	0.026204	0.000674	0.000331	0.002353	0.001983	0.000925	0.000459	0.018388	0.020815	0.012282	0.002324	0.000104	0.000091	0.000430	0.007567	0.000112	0.005075	0.029837	0.008674	0.014987	0.003715	0.015695	0.018778	0.003133	0.003552	0.017476	0.018325	0.033675	0.036698	0.000508	0.022765	0.008127	0.012438	0.007998	0.011800	0.000575	0.000138	0.003675	0.001455	0.031658	0.004932	0.000261	0.003265	0.000085	0.009585	0.031725	0.001310	0.000087	0.017846	0.001297	0.012547	0.006539	0.024850	0.000095	0.000527	0.011112	0.029868	0.002219	0.000182	0.007637	0.000917	0.012033	0.000535	0.000445	0.025059	0.027897	0.020801	0.017916	0.001731	0.001762	0.012574	0.000228	0.005176	0.013090	0.005762	0.000127	0.033394	0.002037	0.000000	0.000162	0.033933	0.031815
+
+
+1000	0.016595	0.000189	0.000521	0.010115	0.001477	0.025114	0.000796	0.006030	0.006294	0.000807	0.007696	0.006937	0.000091	0.002707	0.000342	0.004018	0.005176	0.003948	0.000159	0.047069	0.044182	0.000059	0.010253	0.000161	0.015706	0.000121	0.000283	0.007175	0.032043	0.044279	0.034835	0.024211	0.001834	0.006120	0.000280	0.000639	0.000021	0.007596	0.000038	0.000524	0.004333	0.019782	0.028618	0.000018	0.002014	0.000037	0.000162	0.034727	0.000019	0.008533	0.003738	0.012698	0.004783	0.000281	0.003416	0.003262	0.000093	0.000126	0.003358	0.000993	0.000457	0.016596	0.012000	0.000612	0.001457	0.025007	0.005477	0.039107	0.000138	0.017844	0.001086	0.010415	0.042751	0.000972	0.001748	0.007409	0.001818	0.011089	0.045567	0.008974	0.009325	0.034201	0.000115	0.000022	0.000589	0.003696	0.026227	0.015852	0.000018	0.051277	0.012050	0.006880	0.000028	0.051297	0.000058	0.029208	0.000228	0.000000	0.000940	0.000028
+
+
+1000	0.002117	0.009593	0.000920	0.018109	0.010448	0.001572	0.003799	0.003936	0.015499	0.015455	0.018607	0.009926	0.017615	0.002164	0.026470	0.020966	0.001807	0.022515	0.016017	0.001879	0.000877	0.003124	0.002901	0.001678	0.001433	0.016566	0.013741	0.018644	0.005263	0.000402	0.000366	0.001373	0.007068	0.000418	0.006980	0.028409	0.007436	0.021034	0.004220	0.011443	0.024375	0.005220	0.006964	0.011956	0.016972	0.018633	0.026481	0.001172	0.014286	0.013930	0.013082	0.013819	0.017823	0.001217	0.000475	0.007028	0.002262	0.023837	0.005311	0.000725	0.005222	0.000348	0.015632	0.024394	0.002837	0.000354	0.023588	0.002902	0.012760	0.010463	0.020668	0.000372	0.001588	0.009089	0.028598	0.005005	0.000570	0.013389	0.002309	0.015719	0.001586	0.001407	0.020357	0.016432	0.022166	0.023551	0.004282	0.004325	0.009529	0.000683	0.007344	0.019456	0.005674	0.000459	0.018705	0.003940	0.027424	0.000542	0.000000	0.017971
+
+
+1000	0.000287	0.010553	0.000284	0.006135	0.005602	0.000163	0.001725	0.000981	0.005776	0.012047	0.006972	0.003243	0.034767	0.000510	0.036712	0.010353	0.000316	0.011990	0.025035	0.000157	0.000058	0.003417	0.000530	0.000998	0.000155	0.026773	0.016556	0.007161	0.000738	0.000019	0.000018	0.000126	0.003390	0.000040	0.005867	0.031066	0.016606	0.008374	0.006092	0.010329	0.012513	0.000909	0.001145	0.034671	0.010625	0.050999	0.047282	0.000096	0.044046	0.004471	0.005943	0.003861	0.007741	0.000523	0.000058	0.002429	0.001841	0.044405	0.001836	0.000162	0.003280	0.000022	0.004691	0.026732	0.000926	0.000019	0.011025	0.000312	0.017665	0.002405	0.017382	0.000028	0.000128	0.006029	0.022275	0.001133	0.000094	0.003865	0.000213	0.005430	0.000214	0.000116	0.036572	0.050994	0.022439	0.012581	0.000590	0.000710	0.024954	0.000039	0.001748	0.007720	0.010314	0.000022	0.044025	0.000532	0.044302	0.000028	0.030963	0.000000
+
diff --git a/docs/mllib-clustering-pic.md b/docs/mllib-clustering-pic.md
deleted file mode 100644
index 826e56cec1304..0000000000000
--- a/docs/mllib-clustering-pic.md
+++ /dev/null
@@ -1,30 +0,0 @@
----
-layout: global
-title: Clustering - MLlib
-displayTitle: <a href="mllib-guide.html">MLlib</a> - Power Iteration Clustering
----
-
-* Table of contents
-{:toc}
-
-
-## Power Iteration Clustering
-
-Power iteration clustering is a scalable and efficient algorithm for clustering points given pointwise mutual affinity values.  Internally the algorithm:
-
-* computes the Gaussian distance between all pairs of points and represents these distances in an Affinity Matrix
-* calculates a Normalized Affinity Matrix
-* calculates the principal eigenvalue and eigenvector
-* Clusters each of the input points according to their principal eigenvector component value
-
-Details of this algorithm are found within [Power Iteration Clustering, Lin and Cohen]{www.icml2010.org/papers/387.pdf}
-
-Example outputs for a dataset inspired by the paper - but with five clusters instead of three- have he following output from our implementation:
-
-<p style="text-align: center;">
-  <img src="img/PIClusteringFiveCirclesInputsAndOutputs.png"
-       title="The Property Graph"
-       alt="The Property Graph"
-       width="50%" />
-  <!-- Images are downsized intentionally to improve quality on retina displays -->
-</p>
\ No newline at end of file
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 5f582661ee325..413b824e369da 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -34,8 +34,25 @@ a given dataset, the algorithm returns the best clustering result).
 * *initializationSteps* determines the number of steps in the k-means\|\| algorithm.
 * *epsilon* determines the distance threshold within which we consider k-means to have converged. 
 
-[Power Iteration Clustering](mllib-clustering-pic.md) that uses the Power Iteration method combined with KMeans clustering to 
-cluster points based on a Gaussian measure of the input data pairwise similarity.
+### Power Iteration Clustering
+
+Power iteration clustering is a scalable and efficient algorithm for clustering points given pointwise mutual affinity values.  Internally the algorithm:
+
+* accepts a [Graph](https://spark.apache.org/docs/0.9.2/api/graphx/index.html#org.apache.spark.graphx.Graph) that represents a  normalized pairwise affinity between all input points.
+* calculates the principal eigenvalue and eigenvector
+* Clusters each of the input points according to their principal eigenvector component value
+
+Details of this algorithm are found within [Power Iteration Clustering, Lin and Cohen]{www.icml2010.org/papers/387.pdf}
+
+Example outputs for a dataset inspired by the paper - but with five clusters instead of three- have he following output from our implementation:
+
+<p style="text-align: center;">
+  <img src="img/PIClusteringFiveCirclesInputsAndOutputs.png"
+       title="The Property Graph"
+       alt="The Property Graph"
+       width="50%" />
+  <!-- Images are downsized intentionally to improve quality on retina displays -->
+</p>
 
 ### Examples
 
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 05489bc45982d..12f5d003ccba5 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -108,13 +108,6 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
-<!--    <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-graphx_${scala.binary.version}</artifactId>
-      <version>${project.version}</version>
-      <type>test-jar</type>
-      <scope>test</scope>
-    </dependency> -->
   </dependencies>
   <profiles>
     <profile>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 22117a256e16a..6039b78311d0e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -17,133 +17,195 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
-import org.apache.log4j.Logger
-import org.apache.spark.SparkContext
+import scala.language.existentials
+
+import breeze.linalg.{DenseVector => BDV}
+import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
 
-import scala.language.existentials
-
 /**
  * Implements the scalable graph clustering algorithm Power Iteration Clustering (see
  * www.icml2010.org/papers/387.pdf).  From the abstract:
  *
- * The input data is first transformed to a normalized Affinity Matrix via Gaussian pairwise
- * distance calculations. Power iteration is then used to find a dimensionality-reduced
+ * The input data is normalized pair-wise similarity matrix of the data.
+ * Power iteration is used to find a dimensionality-reduced
  * representation.  The resulting pseudo-eigenvector provides effective clustering - as
  * performed by Parallel KMeans.
+ *
+ * @param k  Number of clusters to create
+ * @param maxIterations Number of iterations of the PIC algorithm
+ *                      that calculates primary PseudoEigenvector and Eigenvalue
+ * @return Tuple of (Seq[(Cluster Id,Cluster Center)],
+ *         Seq[(VertexId, ClusterID Membership)]
  */
-object PowerIterationClustering {
+class PowerIterationClustering(
+                                        private var k: Int,
+                                        private var maxIterations: Int)
+  extends Serializable with Logging {
 
-  private val logger = Logger.getLogger(getClass.getName())
+  import PowerIterationClustering._
 
-  type LabeledPoint = (VertexId, BDV[Double])
-  type Points = Seq[LabeledPoint]
-  type DGraph = Graph[Double, Double]
-  type IndexedVector[Double] = (Long, BDV[Double])
-
-  // Terminate iteration when norm changes by less than this value
-  val defaultMinNormChange: Double = 1e-11
-
-  // Default number of iterations for PIC loop
-  val defaultIterations: Int = 20
+  /**
+   * Set the number of clusters
+   */
+  def setK(k: Int) = this.k = k
 
-  // Do not allow divide by zero: change to this value instead
-  val defaultDivideByZeroVal: Double = 1e-15
+  /**
+   * Set maximum number of iterations of the power iteration loop
+   */
+  def setMaxIterations(maxIterations: Int) = this.maxIterations = maxIterations
 
-  // Default number of runs by the KMeans.run() method
-  val defaultKMeansRuns = 10
+  /**
+   *
+   * Run the Power Iteration Clustering algorithm
+   *
+   * @param affinityRdd  Set of Pairwise affinities of input points in following format:
+   *                     (SrcPointId, DestPointId, Affinity Value)
+   *                     SrcPointId : Long = Label of Source Point of Pair
+   *                     DestPointId : Long = Label of Destination Point Pair
+   *                     Note: this is a directed graph so if
+   *                     AffinityValue: Double = Scalar measure of
+   *                     inter-point affinity
+   *                     Note: this is a Directed Graph
+   *                     (a) In general: (a,b, affinity(a,b)) != (b,a,affinity(b,a))
+   *                     (b) If the input graph were symmetric, it is still necessary to
+   *                     provide both (a,b, affinityAB) and (b,a, affinityAB)
+   *                     where affinityAB is the identical bidirectional affinity between (a,b)
+   *
+   * @return Tuple of (Seq[(Cluster Id,Cluster Center)],
+   *         RDD[(VertexId, ClusterID Membership)]
+   */
+  def run(affinityRdd: RDD[(Long, Long, Double)])
+  : (Seq[(Int, Double)], RDD[((Long, Double), Int)]) = {
+    val edgesRdd = affinityRdd.map { case (v1, v2, vect) =>
+      Edge(v1, v2, vect)
+    }
+    val g: Graph[Double, Double] = Graph.fromEdges(edgesRdd, -1L)
+    run(g)
+  }
 
   /**
    *
-   * Run a Power Iteration Clustering
+   * Run the Power Iteration Clustering algorithm
    *
-   * @param sc  Spark Context
-   * @param G   Affinity Matrix in a Sparse Graph structure
-   * @param nClusters  Number of clusters to create
-   * @param nIterations Number of iterations of the PIC algorithm
-   *                    that calculates primary PseudoEigenvector and Eigenvalue
-   * @param nRuns  Number of runs for the KMeans clustering
+   * @param g  The affinity matrix in a Sparse Graph structure
+   *           The Graph Vertices are the VertexId's associated with each input Data point
+   *             (Long type)
+   *           The Graph Edges are the Normalized Affinities between pairs of points
+   *           (srcId = from Point, dstId = toPoint, attr = affinity (Double type))
+   *           E.g. (3232L, 1122L, 0.0233442)
    * @return Tuple of (Seq[(Cluster Id,Cluster Center)],
-   *         Seq[(VertexId, ClusterID Membership)]
+   *         RDD[(VertexId, ClusterID Membership)]
    */
-  def run(sc: SparkContext,
-          G: Graph[Double, Double],
-          nClusters: Int,
-          nIterations: Int = defaultIterations,
-          nRuns: Int = defaultKMeansRuns)
-  : (Seq[(Int, Vector)], Seq[((VertexId, Vector), Int)]) = {
-    val (gUpdated, lambda, vt) = getPrincipalEigen(sc, G, nIterations)
-    // TODO: avoid local collect and then sc.parallelize.
-    val localVt = vt.collect.sortBy(_._1)
-    val vectRdd = sc.parallelize(localVt.map(v => (v._1, Vectors.dense(v._2))))
+  def run(g: Graph[Double, Double]) = {
+
+    val (gUpdated, lambda, vt) = getPrincipalEigen(g, maxIterations)
+    val sc = g.vertices.sparkContext
+    val vectRdd = vt.map(v => (v._1, Vectors.dense(v._2)))
     vectRdd.cache()
+    val nRuns = defaultKMeansRuns
     val model = KMeans.train(vectRdd.map {
       _._2
-    }, nClusters, nRuns)
+    }, k, nRuns)
     vectRdd.unpersist()
-    if (logger.isDebugEnabled) {
-      logger.debug(s"Eigenvalue = $lambda EigenVector: ${localVt.mkString(",")}")
+
+    var localVt: Seq[(Long, Double)] = null // Only used when in trace logging mode
+    if (isTraceEnabled) {
+      localVt = vt.collect.sortBy(_._1)
+      logDebug(s"Eigenvalue = $lambda EigenVector: ${
+        localVt.mkString(",")
+      }")
     }
     val estimates = vectRdd.zip(model.predict(vectRdd.map(_._2)))
-    if (logger.isDebugEnabled) {
-      logger.debug(s"lambda=$lambda  eigen=${localVt.mkString(",")}")
+      .map { case ((vid, vect), clustId) =>
+      ((vid, vect.toArray.apply(0)), clustId)
+    }
+    if (isTraceEnabled) {
+      logDebug(s"lambda=$lambda  eigen=${
+        localVt.mkString(",")
+      }")
     }
     val ccs = (0 until model.clusterCenters.length).zip(model.clusterCenters)
-    if (logger.isDebugEnabled) {
-      logger.debug(s"Kmeans model cluster centers: ${ccs.mkString(",")}")
+      .map { case (vid, center) =>
+      (vid, center.toArray.apply(0))
     }
-    val estCollected = estimates.collect.sortBy(_._1._1)
-    if (logger.isDebugEnabled) {
-      val clusters = estCollected.map(_._2)
-      val counts = estCollected.groupBy(_._2).mapValues {
-        _.length
-      }
-      logger.debug(s"Cluster counts: Counts: ${counts.mkString(",")}"
-        + s"\nCluster Estimates: ${estCollected.mkString(",")}")
+    if (isTraceEnabled) {
+      logDebug(s"Kmeans model cluster centers: ${
+        ccs.mkString(",")
+      }")
     }
-    (ccs, estCollected)
+    (ccs, estimates)
   }
 
+}
+
+object PowerIterationClustering extends Logging {
+  type LabeledPoint = (VertexId, BDV[Double])
+  type Points = Seq[LabeledPoint]
+  type DGraph = Graph[Double, Double]
+  type IndexedVector[Double] = (Long, BDV[Double])
+
+  // Terminate iteration when norm changes by less than this value
+  val defaultMinNormChange: Double = 1e-11
+
+  // Default number of iterations for PIC loop
+  val defaultIterations: Int = 20
+
+  // Do not allow divide by zero: change to this value instead
+  val defaultDivideByZeroVal: Double = 1e-15
+
+  // Default number of runs by the KMeans.run() method
+  private[mllib] val defaultKMeansRuns = 1
+
 
   /**
    * Create a Graph given an initial Vt0 and a set of Edges that
    * represent the Normalized Affinity Matrix (W)
    */
-  def createGraphFromEdges(sc: SparkContext,
-                           edgesRdd: RDD[Edge[Double]],
-                           nPoints: Int,
-                           optInitialVt: Option[Seq[(VertexId, Double)]] = None) = {
-
-    assert(nPoints > 0, "Must provide number of points from the original dataset")
-    val G = if (optInitialVt.isDefined) {
-      val initialVt = optInitialVt.get
-      val vertsRdd = sc.parallelize(initialVt)
-      Graph(vertsRdd, edgesRdd)
+  private[mllib] def createGraphFromEdges(edgesRdd: RDD[Edge[Double]],
+                                          optInitialVt: Option[Seq[(VertexId, Double)]] = None) = {
+
+    val g = Graph.fromEdges(edgesRdd, -1.0)
+    val outG: Graph[Double, Double] = if (optInitialVt.isDefined) {
+      g.outerJoinVertices(
+        g.vertices.sparkContext.parallelize(optInitialVt.get)) {
+        case (vid, graphAttrib, optVtValue ) =>
+          optVtValue.getOrElse(graphAttrib)
+      }
     } else {
-      Graph.fromEdges(edgesRdd, -1.0)
+      g
     }
-    G
+    outG
+  }
 
+  /**
+   * Creates an initial Vt(0) used within the first iteration of the PIC
+   */
+  private[mllib] def createInitialVector(sc: SparkContext,
+                                         labels: Seq[VertexId],
+                                         rowSums: Seq[Double]) = {
+    val volume = rowSums.fold(0.0) {
+      _ + _
+    }
+    val initialVt = labels.zip(rowSums.map(_ / volume))
+    initialVt
   }
 
   /**
    * Calculate the dominant Eigenvalue and Eigenvector for a given sparse graph
    * using the PIC method
-   * @param sc
-   * @param G  Input Graph representing the Normalized Affinity Matrix (W)
-   * @param nIterations Number of iterations of the PIC algorithm
+   * @param g  Input Graph representing the Normalized Affinity Matrix (W)
+   * @param maxIterations Number of iterations of the PIC algorithm
    * @param optMinNormChange Minimum norm acceleration for detecting convergence
    *                         - indicated as "epsilon" in the PIC paper
    * @return
    */
-  def getPrincipalEigen(sc: SparkContext,
-                        G: DGraph,
-                        nIterations: Int = defaultIterations,
-                        optMinNormChange: Option[Double] = None
-                         ): (DGraph, Double, VertexRDD[Double]) = {
+  private[mllib] def getPrincipalEigen(g: DGraph,
+                                       maxIterations: Int = defaultIterations,
+                                       optMinNormChange: Option[Double] = None
+                                        ): (DGraph, Double, VertexRDD[Double]) = {
 
     var priorNorm = Double.MaxValue
     var norm = Double.MaxValue
@@ -153,49 +215,60 @@ object PowerIterationClustering {
     val DummyVertexId = -99L
     var vnorm: Double = -1.0
     var outG: DGraph = null
-    var prevG: DGraph = G
+    var prevG: DGraph = g
+    // The epsilon calculation is provided by the original paper www.icml2010.org/papers/387.pdf
+    //   as epsilon = 1e-5/(#points)
+    // However that seems quite small for large#points
+    // Instead we use  epsilonPrime = Max(epsilon, 1e-10)
+
     val epsilon = optMinNormChange
-      .getOrElse(1e-5 / G.vertices.count())
-    for (iter <- 0 until nIterations
-         if Math.abs(normAccel) > epsilon) {
+      .getOrElse(math.max(1e-5 / g.vertices.count(), 1e-10))
+    for (iter <- 0 until maxIterations
+         if math.abs(normAccel) > epsilon) {
 
       val tmpEigen = prevG.aggregateMessages[Double](ctx => {
         ctx.sendToSrc(ctx.attr * ctx.srcAttr);
         ctx.sendToDst(ctx.attr * ctx.dstAttr)
       },
         _ + _)
-      if (logger.isDebugEnabled) {
-        logger.debug(s"tmpEigen[$iter]: ${tmpEigen.collect.mkString(",")}\n")
+      if (isTraceEnabled()) {
+        logTrace(s"tmpEigen[$iter]: ${
+          tmpEigen.collect.mkString(",")
+        }\n")
       }
       val vnorm =
         prevG.vertices.map {
           _._2
-        }.fold(0.0) { case (sum, dval) =>
-          sum + Math.abs(dval)
+        }.fold(0.0) {
+          case (sum, dval) =>
+            sum + math.abs(dval)
         }
-      if (logger.isDebugEnabled) {
-        logger.debug(s"vnorm[$iter]=$vnorm")
+      if (isTraceEnabled()) {
+        logTrace(s"vnorm[$iter]=$vnorm")
       }
-      outG = prevG.outerJoinVertices(tmpEigen) { case (vid, wval, optTmpEigJ) =>
-        val normedEig = optTmpEigJ.getOrElse {
-          -1.0
-        } / vnorm
-        if (logger.isDebugEnabled) {
-          logger.debug(s"Updating vertex[$vid] from $wval to $normedEig")
-        }
-        normedEig
+      outG = prevG.outerJoinVertices(tmpEigen) {
+        case (vid, wval, optTmpEigJ) =>
+          val normedEig = optTmpEigJ.getOrElse {
+            -1.0
+          } / vnorm
+          if (isTraceEnabled()) {
+            logTrace(s"Updating vertex[$vid] from $wval to $normedEig")
+          }
+          normedEig
       }
       prevG = outG
 
-      if (logger.isDebugEnabled) {
+      if (isTraceEnabled()) {
         val localVertices = outG.vertices.collect
         val graphSize = localVertices.size
-        print(s"Vertices[$iter]: ${localVertices.mkString(",")}\n")
+        print(s"Vertices[$iter]: ${
+          localVertices.mkString(",")
+        }\n")
       }
       normVelocity = vnorm - priorNorm
       normAccel = normVelocity - priorNormVelocity
-      if (logger.isDebugEnabled) {
-        logger.debug(s"normAccel[$iter]= $normAccel")
+      if (isTraceEnabled()) {
+        logTrace(s"normAccel[$iter]= $normAccel")
       }
       priorNorm = vnorm
       priorNormVelocity = vnorm - priorNorm
@@ -203,18 +276,4 @@ object PowerIterationClustering {
     (outG, vnorm, outG.vertices)
   }
 
-  /**
-   * Creates an initial Vt(0) used within the first iteration of the PIC
-   */
-  private[mllib] def createInitialVector(sc: SparkContext,
-                                         labels: Seq[VertexId],
-                                         rowSums: Seq[Double]) = {
-    val volume = rowSums.fold(0.0) {
-      _ + _
-    }
-    val initialVt = labels.zip(rowSums.map(_ / volume))
-    initialVt
-  }
-
-
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
index 5bc319602bafa..1208ef95781ac 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala
@@ -19,16 +19,20 @@ package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
 import org.apache.log4j.Logger
-import org.apache.spark.SparkContext
+import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.graphx.{EdgeRDD, Edge, Graph}
 import org.apache.spark.mllib.clustering.PowerIterationClustering.{LabeledPoint, Points, IndexedVector}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.scalatest.FunSuite
 
+import scala.language.existentials
+import scala.language.implicitConversions
+
 import scala.util.Random
 
-class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext {
+class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext
+  with Logging {
 
   val logger = Logger.getLogger(getClass.getName)
 
@@ -59,8 +63,26 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext
     })
 
     val nVertices = vertices.length
-    val G = createGaussianAffinityMatrix(sc, vertices)
-    val (ccenters, estCollected) = PIC.run(sc, G, nClusters, nIterations)
+    val g = createGaussianAffinityMatrix(sc, vertices)
+//    val PIC = new PowerIterationClustering(nClusters, nIterations)
+    val affinityRdd = g.edges.map { e =>
+      (e.srcId, e.dstId, e.attr)
+    }
+    val PIC = new PowerIterationClustering(nClusters, nIterations)
+    val (ccenters, estimates) = PIC.run(affinityRdd)
+    val estCollected = estimates.collect.sortBy(_._1._1)
+    if (isTraceEnabled) {
+      val clusters = estCollected.map(_._2)
+      val counts = estCollected.groupBy(_._2).mapValues {
+        _.length
+      }
+      logDebug(s"Cluster counts: Counts: ${
+        counts.mkString(",")
+      }"
+        + s"\nCluster Estimates: ${
+        estCollected.mkString(",")
+      }")
+    }
     logger.info(s"Cluster centers: ${ccenters.mkString(",")} " +
       s"\nEstimates: ${estCollected.mkString("[", ",", "]")}")
     assert(ccenters.size == circleSpecs.length, "Did not get correct number of centers")
@@ -69,8 +91,7 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext
 
 }
 
-object PowerIterationClusteringSuite {
-  val logger = Logger.getLogger(getClass.getName)
+object PowerIterationClusteringSuite extends Logging {
   val A = Array
   val PIC = PowerIterationClustering
 
@@ -112,7 +133,7 @@ object PowerIterationClusteringSuite {
       circlePoints
     }
     val points = circles.flatten.sortBy(_.label)
-    logger.info(printPoints(points))
+    logInfo(printPoints(points))
     points
   }
 
@@ -150,22 +171,18 @@ object PowerIterationClusteringSuite {
 
     val (wRdd, rowSums) = createNormalizedAffinityMatrix(sc, points, sigma)
     val initialVt = PIC.createInitialVector(sc, points.map(_._1), rowSums)
-    if (logger.isDebugEnabled) {
-      logger.debug(s"Vt(0)=${
+    if (isTraceEnabled) {
+      logTrace(s"Vt(0)=${
         printVector(new BDV(initialVt.map {
           _._2
         }.toArray))
       }")
     }
-    val edgesRdd = createSparseEdgesRdd(sc, wRdd, minAffinity)
-    val G = PIC.createGraphFromEdges(sc, edgesRdd, points.size, Some(initialVt))
-    if (logger.isDebugEnabled) {
-      logger.debug(printMatrixFromEdges(G.edges))
-    }
-    G
+    val edgesRdd = createSparseEdgesRdd(wRdd, minAffinity)
+    val g = PIC.createGraphFromEdges(edgesRdd, Some(initialVt))
+    g
   }
 
-
   /**
    * Create the normalized affinity matrix "W" given a set of Points
    *
@@ -195,8 +212,8 @@ object PowerIterationClusteringSuite {
         (ix, vect)
       }
     }, nVertices)
-    if (logger.isDebugEnabled) {
-      logger.debug(s"Affinity:\n${
+    if (isTraceEnabled) {
+      logTrace(s"Affinity:\n${
         printMatrix(affinityRddNotNorm.map(_._2), nVertices, nVertices)
       }")
     }
@@ -211,16 +228,16 @@ object PowerIterationClusteringSuite {
         _ / materializedRowSums(rowx)
       })
     }
-    if (logger.isDebugEnabled) {
-      logger.debug(s"W:\n${printMatrix(similarityRdd, nVertices, nVertices)}")
+    if (isTraceEnabled) {
+      println(s"W:\n${printMatrix(similarityRdd, nVertices, nVertices)}")
     }
     (similarityRdd, materializedRowSums)
   }
 
-  private[mllib] def printMatrix(denseVectorRDD: RDD[LabeledPoint], i: Int, i1: Int) = {
-    denseVectorRDD.collect.map {
+  private[mllib] def printMatrix(denseVectorRDD: RDD[LabeledPoint], i: Int, i1: Int): String = {
+    printMatrix(denseVectorRDD.collect.map {
       case (vid, dvect) => dvect.toArray
-    }.flatten
+    }.flatten, i, i1)
   }
 
 
@@ -245,17 +262,16 @@ object PowerIterationClusteringSuite {
     dist
   }
 
-    /**
+  /**
    * Create a sparse EdgeRDD from an array of densevectors. The elements that
    * are "close to" zero - as configured by the minAffinity value - do not
    * result in an Edge being created.
    *
-   * @param sc
    * @param wRdd
    * @param minAffinity
    * @return
    */
-  private[mllib] def createSparseEdgesRdd(sc: SparkContext, wRdd: RDD[IndexedVector[Double]],
+  private[mllib] def createSparseEdgesRdd(wRdd: RDD[IndexedVector[Double]],
                                           minAffinity: Double = defaultMinAffinity) = {
     val labels = wRdd.map { case (vid, vect) => vid}.collect
     val edgesRdd = wRdd.flatMap { case (vid, vect) =>
@@ -266,15 +282,6 @@ object PowerIterationClusteringSuite {
     edgesRdd
   }
 
-
-  private[mllib] def printMatrixFromEdges(edgesRdd: EdgeRDD[_]) = {
-    val edgec = edgesRdd.collect
-    val sorted = edgec.sortWith { case (e1, e2) =>
-      e1.srcId < e2.srcId || (e1.srcId == e2.srcId && e1.dstId <= e2.dstId)
-    }
-
-  }
-
   private[mllib] def makeNonZero(dval: Double, tol: Double = PIC.defaultDivideByZeroVal) = {
     if (Math.abs(dval) < tol) {
       Math.signum(dval) * tol