diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 5e6322f2a05a1..5c563262e184d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -97,14 +97,31 @@ class PrefixSpan private (
     if (sequences.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
-    val minCount = getMinCount(sequences)
-    val lengthOnePatternsAndCounts = getFreqItemAndCounts(minCount, sequences)
-    val prefixSuffixPairs = getPrefixSuffixPairs(
-      lengthOnePatternsAndCounts.map(_._1).collect(), sequences)
+
+    // Convert min support to a min number of transactions for this dataset
+    val minCount = if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
+
+    val itemCounts = sequences
+      .flatMap(_.distinct.map((_, 1L)))
+      .reduceByKey(_ + _)
+      .filter(_._2 >= minCount)
+
+    val prefixSuffixPairs = {
+      val frequentItems = itemCounts.map(_._1).collect()
+      val candidates = sequences.map { p =>
+        p.filter (frequentItems.contains(_) )
+      }
+      candidates.flatMap { x =>
+        frequentItems.map { y =>
+          val sub = LocalPrefixSpan.getSuffix(y, x)
+          (ArrayBuffer(y), sub)
+        }.filter(_._2.nonEmpty)
+      }
+    }
     prefixSuffixPairs.persist(StorageLevel.MEMORY_AND_DISK)
-    var allPatternAndCounts = lengthOnePatternsAndCounts.map(x => (ArrayBuffer(x._1), x._2))
-    var (smallPrefixSuffixPairs, largePrefixSuffixPairs) =
-      splitPrefixSuffixPairs(prefixSuffixPairs)
+
+    var allPatternAndCounts = itemCounts.map(x => (ArrayBuffer(x._1), x._2))
+    var (smallPrefixSuffixPairs, largePrefixSuffixPairs) = splitPrefixSuffixPairs(prefixSuffixPairs)
     while (largePrefixSuffixPairs.count() != 0) {
       val (nextPatternAndCounts, nextPrefixSuffixPairs) =
         getPatternCountsAndPrefixSuffixPairs(minCount, largePrefixSuffixPairs)
@@ -115,6 +132,7 @@ class PrefixSpan private (
       smallPrefixSuffixPairs ++= smallerPairsPart
       allPatternAndCounts ++= nextPatternAndCounts
     }
+
     if (smallPrefixSuffixPairs.count() > 0) {
       val projectedDatabase = smallPrefixSuffixPairs
         .map(x => (x._1.toSeq, x._2))
@@ -189,29 +207,6 @@ class PrefixSpan private (
     (patternAndCounts, nextPrefixSuffixPairs)
   }
 
-  /**
-   * Get the minimum count (sequences count * minSupport).
-   * @param sequences input data set, contains a set of sequences,
-   * @return minimum count,
-   */
-  private def getMinCount(sequences: RDD[Array[Int]]): Long = {
-    if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
-  }
-
-  /**
-   * Generates frequent items by filtering the input data using minimal count level.
-   * @param minCount the absolute minimum count
-   * @param sequences original sequences data
-   * @return array of item and count pair
-   */
-  private def getFreqItemAndCounts(
-      minCount: Long,
-      sequences: RDD[Array[Int]]): RDD[(Int, Long)] = {
-    sequences.flatMap(_.distinct.map((_, 1L)))
-      .reduceByKey(_ + _)
-      .filter(_._2 >= minCount)
-  }
-
   /**
    * Get the frequent prefixes and suffix pairs.
    * @param frequentPrefixes frequent prefixes
@@ -221,15 +216,6 @@ class PrefixSpan private (
   private def getPrefixSuffixPairs(
       frequentPrefixes: Array[Int],
       sequences: RDD[Array[Int]]): RDD[(ArrayBuffer[Int], Array[Int])] = {
-    val filteredSequences = sequences.map { p =>
-      p.filter (frequentPrefixes.contains(_) )
-    }
-    filteredSequences.flatMap { x =>
-      frequentPrefixes.map { y =>
-        val sub = LocalPrefixSpan.getSuffix(y, x)
-        (ArrayBuffer(y), sub)
-      }.filter(_._2.nonEmpty)
-    }
   }
 
   /**