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