diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index 9591c7966e06a..96baac07cb5d4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -17,15 +17,23 @@
 
 package org.apache.spark.mllib.fpm
 
+import java.lang.{Iterable => JavaIterable}
 import java.{util => ju}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
+import scala.reflect.ClassTag
 
-import org.apache.spark.{SparkException, HashPartitioner, Logging, Partitioner}
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
 
-class FPGrowthModel(val freqItemsets: RDD[(Array[String], Long)]) extends Serializable
+class FPGrowthModel[Item](val freqItemsets: RDD[(Array[Item], Long)]) extends Serializable {
+  def javaFreqItemsets(): JavaRDD[(Array[Item], Long)] = {
+    freqItemsets.toJavaRDD()
+  }
+}
 
 /**
  * This class implements Parallel FP-growth algorithm to do frequent pattern matching on input data.
@@ -69,7 +77,7 @@ class FPGrowth private (
    * @param data input data set, each element contains a transaction
    * @return an [[FPGrowthModel]]
    */
-  def run(data: RDD[Array[String]]): FPGrowthModel = {
+  def run[Item: ClassTag, Basket <: Iterable[Item]](data: RDD[Basket]): FPGrowthModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
@@ -77,24 +85,28 @@ class FPGrowth private (
     val minCount = math.ceil(minSupport * count).toLong
     val numParts = if (numPartitions > 0) numPartitions else data.partitions.length
     val partitioner = new HashPartitioner(numParts)
-    val freqItems = genFreqItems(data, minCount, partitioner)
-    val freqItemsets = genFreqItemsets(data, minCount, freqItems, partitioner)
+    val freqItems = genFreqItems[Item, Basket](data, minCount, partitioner)
+    val freqItemsets = genFreqItemsets[Item, Basket](data, minCount, freqItems, partitioner)
     new FPGrowthModel(freqItemsets)
   }
 
+  def run[Item: ClassTag, Basket <: JavaIterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item] = {
+    this.run(data.rdd.map(_.asScala))
+  }
+
   /**
    * Generates frequent items by filtering the input data using minimal support level.
    * @param minCount minimum count for frequent itemsets
    * @param partitioner partitioner used to distribute items
    * @return array of frequent pattern ordered by their frequencies
    */
-  private def genFreqItems(
-      data: RDD[Array[String]],
+  private def genFreqItems[Item: ClassTag, Basket <: Iterable[Item]](
+      data: RDD[Basket],
       minCount: Long,
-      partitioner: Partitioner): Array[String] = {
+      partitioner: Partitioner): Array[Item] = {
     data.flatMap { t =>
       val uniq = t.toSet
-      if (t.length != uniq.size) {
+      if (t.size != uniq.size) {
         throw new SparkException(s"Items in a transaction must be unique but got ${t.toSeq}.")
       }
       t
@@ -114,11 +126,11 @@ class FPGrowth private (
    * @param partitioner partitioner used to distribute transactions
    * @return an RDD of (frequent itemset, count)
    */
-  private def genFreqItemsets(
-      data: RDD[Array[String]],
+  private def genFreqItemsets[Item: ClassTag, Basket <: Iterable[Item]](
+      data: RDD[Basket],
       minCount: Long,
-      freqItems: Array[String],
-      partitioner: Partitioner): RDD[(Array[String], Long)] = {
+      freqItems: Array[Item],
+      partitioner: Partitioner): RDD[(Array[Item], Long)] = {
     val itemToRank = freqItems.zipWithIndex.toMap
     data.flatMap { transaction =>
       genCondTransactions(transaction, itemToRank, partitioner)
@@ -139,13 +151,13 @@ class FPGrowth private (
    * @param partitioner partitioner used to distribute transactions
    * @return a map of (target partition, conditional transaction)
    */
-  private def genCondTransactions(
-      transaction: Array[String],
-      itemToRank: Map[String, Int],
+  private def genCondTransactions[Item: ClassTag, Basket <: Iterable[Item]](
+      transaction: Basket,
+      itemToRank: Map[Item, Int],
       partitioner: Partitioner): mutable.Map[Int, Array[Int]] = {
     val output = mutable.Map.empty[Int, Array[Int]]
     // Filter the basket by frequent items pattern and sort their ranks.
-    val filtered = transaction.flatMap(itemToRank.get)
+    val filtered = transaction.flatMap(itemToRank.get).toArray
     ju.Arrays.sort(filtered)
     val n = filtered.length
     var i = n - 1
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
index 71ef60da6dd32..67dc246abfb24 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/FPGrowthSuite.scala
@@ -22,7 +22,8 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class FPGrowthSuite extends FunSuite with MLlibTestSparkContext {
 
-  test("FP-Growth") {
+
+  test("FP-Growth using String type") {
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -30,7 +31,7 @@ class FPGrowthSuite extends FunSuite with MLlibTestSparkContext {
       "x z y m t s q e",
       "z",
       "x z y r q t p")
-      .map(_.split(" "))
+      .map(_.split(" ").toSeq)
     val rdd = sc.parallelize(transactions, 2).cache()
 
     val fpg = new FPGrowth()
@@ -38,13 +39,13 @@ class FPGrowthSuite extends FunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .run(rdd)
+      .run[String, Seq[String]](rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .run(rdd)
+      .run[String, Seq[String]](rdd)
     val freqItemsets3 = model3.freqItemsets.collect().map { case (items, count) =>
       (items.toSet, count)
     }
@@ -61,13 +62,59 @@ class FPGrowthSuite extends FunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .run(rdd)
+      .run[String, Seq[String]](rdd)
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .run(rdd)
+      .run[String, Seq[String]](rdd)
     assert(model1.freqItemsets.count() === 625)
   }
+
+  test("FP-Growth using Int type") {
+    val transactions = Seq(
+      "1 2 3",
+      "1 2 3 4",
+      "5 4 3 2 1",
+      "6 5 4 3 2 1",
+      "2 4",
+      "1 3",
+      "1 7")
+      .map(_.split(" ").map(_.toInt).toList)
+    val rdd = sc.parallelize(transactions, 2).cache()
+
+    val fpg = new FPGrowth()
+
+    val model6 = fpg
+      .setMinSupport(0.9)
+      .setNumPartitions(1)
+      .run[Int, List[Int]](rdd)
+    assert(model6.freqItemsets.count() === 0)
+
+    val model3 = fpg
+      .setMinSupport(0.5)
+      .setNumPartitions(2)
+      .run[Int, List[Int]](rdd)
+    val freqItemsets3 = model3.freqItemsets.collect().map { case (items, count) =>
+      (items.toSet, count)
+    }
+    val expected = Set(
+      (Set(1), 6L), (Set(2), 5L), (Set(3), 5L), (Set(4), 4L),
+      (Set(1, 2), 4L), (Set(1, 3), 5L), (Set(2, 3), 4L),
+      (Set(2, 4), 4L), (Set(1, 2, 3), 4L))
+    assert(freqItemsets3.toSet === expected)
+
+    val model2 = fpg
+      .setMinSupport(0.3)
+      .setNumPartitions(4)
+      .run[Int, List[Int]](rdd)
+    assert(model2.freqItemsets.count() === 15)
+
+    val model1 = fpg
+      .setMinSupport(0.1)
+      .setNumPartitions(8)
+      .run[Int, List[Int]](rdd)
+    assert(model1.freqItemsets.count() === 65)
+  }
 }