[SPARK-5519][MLLIB] add user guide with example code for fp-growth

The API is still not very Java-friendly because `Array[Item]` in `freqItemsets` is recognized as `Object` in Java. We might want to define a case class to wrap the return pair to make it Java friendly.

Author: Xiangrui Meng <meng@databricks.com>

Closes #4661 from mengxr/SPARK-5519 and squashes the following commits:

58ccc25 [Xiangrui Meng] add user guide with example code for fp-growth

docs/mllib-frequent-pattern-mining.md

+---
+layout: global
+title: Frequent Pattern Mining - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Frequent Pattern Mining
+---
+
+Mining frequent items, itemsets, subsequences, or other substructures is usually among the
+first steps to analyze a large-scale dataset, which has been an active research topic in
+data mining for years.
+We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning)
+for more information.
+MLlib provides a parallel implementation of FP-growth,
+a popular algorithm to mining frequent itemsets.
+
+## FP-growth
+
+The FP-growth algorithm is described in the paper
+[Han et al., Mining frequent patterns without candidate generation](http://dx.doi.org/10.1145/335191.335372),
+where "FP" stands for frequent pattern.
+Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items.
+Different from [Apriori-like](http://en.wikipedia.org/wiki/Apriori_algorithm) algorithms designed for the same purpose,
+the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets
+explicitly, which are usually expensive to generate.
+After the second step, the frequent itemsets can be extracted from the FP-tree.
+In MLlib, we implemented a parallel version of FP-growth called PFP,
+as described in [Li et al., PFP: Parallel FP-growth for query recommendation](http://dx.doi.org/10.1145/1454008.1454027).
+PFP distributes the work of growing FP-trees based on the suffices of transactions,
+and hence more scalable than a single-machine implementation.
+We refer users to the papers for more details.
+
+MLlib's FP-growth implementation takes the following (hyper-)parameters:
+
+* `minSupport`: the minimum support for an itemset to be identified as frequent.
+  For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6.
+* `numPartitions`: the number of partitions used to distribute the work.
+
+**Examples**
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
+FP-growth algorithm.
+It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
+Calling `FPGrowth.run` with transactions returns an
+[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
+that stores the frequent itemsets with their frequencies.
+
+{% highlight scala %}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
+
+val transactions: RDD[Array[String]] = ...
+
+val fpg = new FPGrowth()
+  .setMinSupport(0.2)
+  .setNumPartitions(10)
+val model = fpg.run(transactions)
+
+model.freqItemsets.collect().foreach { case (itemset, freq) =>
+  println(itemset.mkString("[", ",", "]") + ", " + freq)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
+FP-growth algorithm.
+It take an `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
+Calling `FPGrowth.run` with transactions returns an
+[`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
+that stores the frequent itemsets with their frequencies.
+
+{% highlight java %}
+import java.util.Arrays;
+import java.util.List;
+
+import scala.Tuple2;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.mllib.fpm.FPGrowth;
+import org.apache.spark.mllib.fpm.FPGrowthModel;
+
+JavaRDD<List<String>> transactions = ...
+
+FPGrowth fpg = new FPGrowth()
+  .setMinSupport(0.2)
+  .setNumPartitions(10);
+
+FPGrowthModel<String> model = fpg.run(transactions);
+
+for (Tuple2<Object, Long> s: model.javaFreqItemsets().collect()) {
+   System.out.println("(" + Arrays.toString((Object[]) s._1()) + "): " + s._2());
+}
+{% endhighlight %}
+
+</div>
+</div>

docs/mllib-guide.md

@@ -34,6 +34,8 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
   * singular value decomposition (SVD)
   * principal component analysis (PCA)
 * [Feature extraction and transformation](mllib-feature-extraction.html)
+* [Frequent pattern mining](mllib-frequent-pattern-mining.html)
+  * FP-growth
 * [Optimization (developer)](mllib-optimization.html)
   * stochastic gradient descent
   * limited-memory BFGS (L-BFGS)

examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+
+import scala.Tuple2;
+
+import com.google.common.collect.Lists;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.FPGrowth;
+import org.apache.spark.mllib.fpm.FPGrowthModel;
+
+/**
+ * Java example for mining frequent itemsets using FP-growth.
+ */
+public class JavaFPGrowthExample {
+
+  public static void main(String[] args) {
+    SparkConf sparkConf = new SparkConf().setAppName("JavaFPGrowthExample");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+
+
+    // TODO: Read a user-specified input file.
+    @SuppressWarnings("unchecked")
+    JavaRDD<ArrayList<String>> transactions = sc.parallelize(Lists.newArrayList(
+      Lists.newArrayList("r z h k p".split(" ")),
+      Lists.newArrayList("z y x w v u t s".split(" ")),
+      Lists.newArrayList("s x o n r".split(" ")),
+      Lists.newArrayList("x z y m t s q e".split(" ")),
+      Lists.newArrayList("z".split(" ")),
+      Lists.newArrayList("x z y r q t p".split(" "))), 2);
+
+    FPGrowth fpg = new FPGrowth()
+      .setMinSupport(0.3);
+    FPGrowthModel<String> model = fpg.run(transactions);
+
+    for (Tuple2<Object, Long> s: model.javaFreqItemsets().collect()) {
+      System.out.println(Arrays.toString((Object[]) s._1()) + ", " + s._2());
+    }
+
+    sc.stop();
+  }
+}

examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.mllib.fpm.FPGrowth
+import org.apache.spark.{SparkContext, SparkConf}
+
+/**
+ * Example for mining frequent itemsets using FP-growth.
+ */
+object FPGrowthExample {
+
+  def main(args: Array[String]) {
+    val conf = new SparkConf().setAppName("FPGrowthExample")
+    val sc = new SparkContext(conf)
+
+    // TODO: Read a user-specified input file.
+    val transactions = sc.parallelize(Seq(
+      "r z h k p",
+      "z y x w v u t s",
+      "s x o n r",
+      "x z y m t s q e",
+      "z",
+      "x z y r q t p").map(_.split(" ")), numSlices = 2)
+
+    val fpg = new FPGrowth()
+      .setMinSupport(0.3)
+    val model = fpg.run(transactions)
+
+    model.freqItemsets.collect().foreach { case (itemset, freq) =>
+      println(itemset.mkString("[", ",", "]") + ", " + freq)
+    }
+
+    sc.stop()
+  }
+}