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<h1 class="title"><a href="mllib-guide.html">MLlib</a> - Frequent Pattern Mining</h1>
<p>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 <a href="http://en.wikipedia.org/wiki/Association_rule_learning">association rule learning</a>
for more information.
MLlib provides a parallel implementation of FP-growth,
a popular algorithm to mining frequent itemsets.</p>
<h2 id="fp-growth">FP-growth</h2>
<p>The FP-growth algorithm is described in the paper
<a href="http://dx.doi.org/10.1145/335191.335372">Han et al., Mining frequent patterns without candidate generation</a>,
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 <a href="http://en.wikipedia.org/wiki/Apriori_algorithm">Apriori-like</a> 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 <a href="http://dx.doi.org/10.1145/1454008.1454027">Li et al., PFP: Parallel FP-growth for query recommendation</a>.
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.</p>
<p>MLlib’s FP-growth implementation takes the following (hyper-)parameters:</p>
<ul>
<li><code>minSupport</code>: 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.</li>
<li><code>numPartitions</code>: the number of partitions used to distribute the work.</li>
</ul>
<p><strong>Examples</strong></p>
<div class="codetabs">
<div data-lang="scala">
<p><a href="api/java/org/apache/spark/mllib/fpm/FPGrowth.html"><code>FPGrowth</code></a> implements the
FP-growth algorithm.
It take a <code>JavaRDD</code> of transactions, where each transaction is an <code>Iterable</code> of items of a generic type.
Calling <code>FPGrowth.run</code> with transactions returns an
<a href="api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html"><code>FPGrowthModel</code></a>
that stores the frequent itemsets with their frequencies.</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.rdd.RDD</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.fpm.</span><span class="o">{</span><span class="nc">FPGrowth</span><span class="o">,</span> <span class="nc">FPGrowthModel</span><span class="o">}</span>
<span class="k">val</span> <span class="n">transactions</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Array</span><span class="o">[</span><span class="kt">String</span><span class="o">]]</span> <span class="k">=</span> <span class="o">...</span>
<span class="k">val</span> <span class="n">fpg</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">FPGrowth</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMinSupport</span><span class="o">(</span><span class="mf">0.2</span><span class="o">)</span>
<span class="o">.</span><span class="n">setNumPartitions</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">fpg</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">transactions</span><span class="o">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">freqItemsets</span><span class="o">.</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span> <span class="o">{</span> <span class="n">itemset</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="n">itemset</span><span class="o">.</span><span class="n">items</span><span class="o">.</span><span class="n">mkString</span><span class="o">(</span><span class="s">"["</span><span class="o">,</span> <span class="s">","</span><span class="o">,</span> <span class="s">"]"</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">itemset</span><span class="o">.</span><span class="n">freq</span><span class="o">)</span>
<span class="o">}</span></code></pre></div>
</div>
<div data-lang="java">
<p><a href="api/java/org/apache/spark/mllib/fpm/FPGrowth.html"><code>FPGrowth</code></a> implements the
FP-growth algorithm.
It take an <code>RDD</code> of transactions, where each transaction is an <code>Array</code> of items of a generic type.
Calling <code>FPGrowth.run</code> with transactions returns an
<a href="api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html"><code>FPGrowthModel</code></a>
that stores the frequent itemsets with their frequencies.</p>
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.base.Joiner</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.fpm.FPGrowth</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.fpm.FPGrowthModel</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">List</span><span class="o"><</span><span class="n">String</span><span class="o">>></span> <span class="n">transactions</span> <span class="o">=</span> <span class="o">...</span>
<span class="n">FPGrowth</span> <span class="n">fpg</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">FPGrowth</span><span class="o">()</span>
<span class="o">.</span><span class="na">setMinSupport</span><span class="o">(</span><span class="mf">0.2</span><span class="o">)</span>
<span class="o">.</span><span class="na">setNumPartitions</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span>
<span class="n">FPGrowthModel</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">model</span> <span class="o">=</span> <span class="n">fpg</span><span class="o">.</span><span class="na">run</span><span class="o">(</span><span class="n">transactions</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">FPGrowth</span><span class="o">.</span><span class="na">FreqItemset</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="nl">itemset:</span> <span class="n">model</span><span class="o">.</span><span class="na">freqItemsets</span><span class="o">().</span><span class="na">toJavaRDD</span><span class="o">().</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"["</span> <span class="o">+</span> <span class="n">Joiner</span><span class="o">.</span><span class="na">on</span><span class="o">(</span><span class="s">","</span><span class="o">).</span><span class="na">join</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="na">javaItems</span><span class="o">())</span> <span class="o">+</span> <span class="s">"], "</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="na">freq</span><span class="o">());</span>
<span class="o">}</span></code></pre></div>
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