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 2ae6e9c26d86b..6a0378e611215 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
@@ -36,7 +36,7 @@ import org.apache.spark.storage.StorageLevel
  * :: Experimental ::
  *
  * Model trained by [[FPGrowth]], which holds frequent itemsets.
- * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
+ * @param freqItemsets frequent itemsets, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  */
 @Experimental
@@ -63,11 +63,11 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
 class FPGrowth private (
     private var minSupport: Double,
     private var numPartitions: Int,
-    private var mineSequences: Boolean) extends Logging with Serializable {
+    private var ordered: Boolean) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
-   * as the input data, mineSequences: `false`}.
+   * as the input data, ordered: `false`}.
    */
   def this() = this(0.3, -1, false)
 
@@ -88,10 +88,11 @@ class FPGrowth private (
   }
 
   /**
-   * Indicates whether to mine item-sets or item-sequences (default: false, mine item-sets).
+   * Indicates whether to mine itemsets (unordered) or sequences (ordered) (default: false, mine
+   * itemsets).
    */
-  def setMineSequences(value: Boolean): this.type = {
-    this.mineSequences = value
+  def setOrdered(ordered: Boolean): this.type = {
+    this.ordered = ordered
     this
   }
 
@@ -164,7 +165,7 @@ class FPGrowth private (
     .flatMap { case (part, tree) =>
       tree.extract(minCount, x => partitioner.getPartition(x) == part)
     }.map { case (ranks, count) =>
-      new FreqItemset(ranks.map(i => freqItems(i)).toArray, count)
+      new FreqItemset(ranks.map(i => freqItems(i)).toArray, count, ordered)
     }
   }
 
@@ -182,7 +183,7 @@ class FPGrowth private (
     val output = mutable.Map.empty[Int, Array[Int]]
     // Filter the basket by frequent items pattern
     val filtered = transaction.flatMap(itemToRank.get)
-    if (!this.mineSequences) { // Ignore ordering if not mining sequences
+    if (!this.ordered) {
       ju.Arrays.sort(filtered)
     }
     // Generate conditional transactions
@@ -210,9 +211,11 @@ object FPGrowth {
    * Frequent itemset.
    * @param items items in this itemset. Java users should call [[FreqItemset#javaItems]] instead.
    * @param freq frequency
+   * @param ordered indicates if items represents an itemset (false) or sequence (true)
    * @tparam Item item type
    */
-  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
+  class FreqItemset[Item](val items: Array[Item], val freq: Long, val ordered: Boolean)
+    extends Serializable {
 
     /**
      * Returns items in a Java List.
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 d8887ea2c4ce4..36f381febf688 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 +22,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
 
 
-  test("FP-Growth frequent item-sets using String type") {
+  test("FP-Growth frequent itemsets using String type") {
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -38,14 +38,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     val freqItemsets3 = model3.freqItemsets.collect().map { itemset =>
       (itemset.items.toSet, itemset.freq)
@@ -63,19 +63,19 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 54)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 625)
   }
 
-  test("FP-Growth frequent item-sequences using String type"){
+  test("FP-Growth frequent sequences using String type"){
     val transactions = Seq(
       "r z h k p",
       "z y x w v u t s",
@@ -91,21 +91,21 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model1 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setMineSequences(true)
+      .setOrdered(true)
       .run(rdd)
 
     val expected = Set(
-      (Set("r"), 3L), (Set("s"), 3L), (Set("t"), 3L), (Set("x"), 4L), (Set("y"), 3L),
-      (Set("z"), 5L), (Set("z", "y"), 3L), (Set("x", "t"), 3L), (Set("y", "t"), 3L),
-      (Set("z", "t"), 3L), (Set("z", "y", "t"), 3L)
+      (List("r"), 3L), (List("s"), 3L), (List("t"), 3L), (List("x"), 4L), (List("y"), 3L),
+      (List("z"), 5L), (List("z", "y"), 3L), (List("x", "t"), 3L), (List("y", "t"), 3L),
+      (List("z", "t"), 3L), (List("z", "y", "t"), 3L)
     )
-    val freqItemsets1 = model1.freqItemsets.collect().map { itemset =>
-      (itemset.items.toSet, itemset.freq)
+    val freqItemseqs1 = model1.freqItemsets.collect().map { itemset =>
+      (itemset.items.toList, itemset.freq)
     }.toSet
-    assert(freqItemsets1 === expected)
+    assert(freqItemseqs1 === expected)
   }
 
-  test("FP-Growth frequent item-sets using Int type") {
+  test("FP-Growth frequent itemsets using Int type") {
     val transactions = Seq(
       "1 2 3",
       "1 2 3 4",
@@ -122,14 +122,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model6 = fpg
       .setMinSupport(0.9)
       .setNumPartitions(1)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model6.freqItemsets.count() === 0)
 
     val model3 = fpg
       .setMinSupport(0.5)
       .setNumPartitions(2)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model3.freqItemsets.first().items.getClass === Array(1).getClass,
       "frequent itemsets should use primitive arrays")
@@ -145,14 +145,14 @@ class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model2 = fpg
       .setMinSupport(0.3)
       .setNumPartitions(4)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model2.freqItemsets.count() === 15)
 
     val model1 = fpg
       .setMinSupport(0.1)
       .setNumPartitions(8)
-      .setMineSequences(false)
+      .setOrdered(false)
       .run(rdd)
     assert(model1.freqItemsets.count() === 65)
   }