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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
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| layout | title | displayTitle |
|---|---|---|
| global | Frequent Pattern Mining - MLlib | <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 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, 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 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. 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
FPGrowth 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
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 %}
FPGrowth 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
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> transactions = ...
FPGrowth fpg = new FPGrowth() .setMinSupport(0.2) .setNumPartitions(10);
FPGrowthModel model = fpg.run(transactions);
for (Tuple2<Object, Long> s: model.javaFreqItemsets().collect()) { System.out.println("(" + Arrays.toString((Object[]) s._1()) + "): " + s._2()); } {% endhighlight %}