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 bbdc75532ae6f..8a15a867910a2 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
@@ -103,45 +103,49 @@ class PrefixSpan private (
     // 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
+    // Frequent items -> number of occurrences, all items here satisfy the `minSupport` threshold
+    val freqItemCounts = sequences
       .flatMap(seq => seq.distinct.map(item => (item, 1L)))
       .reduceByKey(_ + _)
       .filter(_._2 >= minCount)
-    var allPatternAndCounts = itemCounts.map(x => (List(x._1), x._2))
 
-    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)
-          (List(y), sub)
-        }.filter(_._2.nonEmpty)
+    // Pairs of (length 1 prefix, suffix consisting of frequent items)
+    val itemSuffixPairs = {
+      val freqItems = freqItemCounts.keys.collect().toSet
+      sequences.flatMap { seq =>
+        freqItems.flatMap { item =>
+          val candidateSuffix = LocalPrefixSpan.getSuffix(item, seq.filter(freqItems.contains(_)))
+          candidateSuffix match {
+            case suffix if !suffix.isEmpty => Some((List(item), suffix))
+            case _ => None
+          }
+        }
       }
     }
-    var (smallPrefixSuffixPairs, largePrefixSuffixPairs) = partitionByProjDBSize(prefixSuffixPairs)
 
-    while (largePrefixSuffixPairs.count() != 0) {
+    // Accumulator for the computed results to be returned
+    var resultsAccumulator = freqItemCounts.map(x => (List(x._1), x._2))
+
+    // Remaining work to be locally and distributively processed respectfully
+    var (pairsForLocal, pairsForDistributed) = partitionByProjDBSize(itemSuffixPairs)
+
+    // Continue processing until no pairs for distributed processing remain (i.e. all prefixes have
+    // projected database sizes <= `maxLocalProjDBSize`)
+    while (pairsForDistributed.count() != 0) {
       val (nextPatternAndCounts, nextPrefixSuffixPairs) =
-        getPatternCountsAndPrefixSuffixPairs(minCount, largePrefixSuffixPairs)
-      largePrefixSuffixPairs.unpersist()
+        getPatternCountsAndPrefixSuffixPairs(minCount, pairsForDistributed)
+      pairsForDistributed.unpersist()
       val (smallerPairsPart, largerPairsPart) = partitionByProjDBSize(nextPrefixSuffixPairs)
-      largePrefixSuffixPairs = largerPairsPart
-      largePrefixSuffixPairs.persist(StorageLevel.MEMORY_AND_DISK)
-      smallPrefixSuffixPairs ++= smallerPairsPart
-      allPatternAndCounts ++= nextPatternAndCounts
+      pairsForDistributed = largerPairsPart
+      pairsForDistributed.persist(StorageLevel.MEMORY_AND_DISK)
+      pairsForLocal ++= smallerPairsPart
+      resultsAccumulator ++= nextPatternAndCounts
     }
 
-    if (smallPrefixSuffixPairs.count() > 0) {
-      val projectedDatabase = smallPrefixSuffixPairs
-        // TODO aggregateByKey
-        .groupByKey()
-      val nextPatternAndCounts = getPatternsInLocal(minCount, projectedDatabase)
-      allPatternAndCounts ++= nextPatternAndCounts
-    }
-    allPatternAndCounts.map { case (pattern, count) => (pattern.toArray, count) }
+    // Process the small projected databases locally
+    resultsAccumulator ++= getPatternsInLocal(minCount, pairsForLocal.groupByKey())
+
+    resultsAccumulator.map { case (pattern, count) => (pattern.toArray, count) }
   }
 
 
@@ -177,8 +181,8 @@ class PrefixSpan private (
    */
   private def getPatternCountsAndPrefixSuffixPairs(
       minCount: Long,
-      prefixSuffixPairs: RDD[(List[Int], Array[Int])]):
-  (RDD[(List[Int], Long)], RDD[(List[Int], Array[Int])]) = {
+      prefixSuffixPairs: RDD[(List[Int], Array[Int])])
+    : (RDD[(List[Int], Long)], RDD[(List[Int], Array[Int])]) = {
     val prefixAndFrequentItemAndCounts = prefixSuffixPairs
       .flatMap { case (prefix, suffix) => suffix.distinct.map(y => ((prefix, y), 1L)) }
       .reduceByKey(_ + _)