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 title: Frequent Pattern Mining
 displayTitle: Frequent Pattern Mining
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 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.
 
 **Table of Contents**
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
 ## FP-Growth
 
 The FP-growth algorithm is described in the paper
 [Han et al., Mining frequent patterns without candidate generation](https://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 `spark.mllib`, we implemented a parallel version of FP-growth called PFP,
 as described in [Li et al., PFP: Parallel FP-growth for query recommendation](https://doi.org/10.1145/1454008.1454027).
 PFP distributes the work of growing FP-trees based on the suffixes of transactions,
 and hence is more scalable than a single-machine implementation.
 We refer users to the papers for more details.
 
 `spark.ml`'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.
 * `minConfidence`: minimum confidence for generating Association Rule. Confidence is an indication of how often an
   association rule has been found to be true. For example, if in the transactions itemset `X` appears 4 times, `X`
   and `Y` co-occur only 2 times, the confidence for the rule `X => Y` is then 2/4 = 0.5. The parameter will not
   affect the mining for frequent itemsets, but specify the minimum confidence for generating association rules
   from frequent itemsets.
 * `numPartitions`: the number of partitions used to distribute the work. By default the param is not set, and
   number of partitions of the input dataset is used.
 
 The `FPGrowthModel` provides:
 
 * `freqItemsets`: frequent itemsets in the format of DataFrame("items"[Array], "freq"[Long])
 * `associationRules`: association rules generated with confidence above `minConfidence`, in the format of 
   DataFrame("antecedent"[Array], "consequent"[Array], "confidence"[Double]).
 * `transform`: For each transaction in `itemsCol`, the `transform` method will compare its items against the antecedents
   of each association rule. If the record contains all the antecedents of a specific association rule, the rule
   will be considered as applicable and its consequents will be added to the prediction result. The transform
   method will summarize the consequents from all the applicable rules as prediction. The prediction column has
   the same data type as `itemsCol` and does not contain existing items in the `itemsCol`.
 
 
 **Examples**
 
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
 Refer to the [Scala API docs](api/scala/org/apache/spark/ml/fpm/FPGrowth.html) for more details.
 
 {% include_example scala/org/apache/spark/examples/ml/FPGrowthExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
 Refer to the [Java API docs](api/java/org/apache/spark/ml/fpm/FPGrowth.html) for more details.
 
 {% include_example java/org/apache/spark/examples/ml/JavaFPGrowthExample.java %}
 </div>
 
 <div data-lang="python" markdown="1">
 Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.fpm.FPGrowth) for more details.
 
 {% include_example python/ml/fpgrowth_example.py %}
 </div>
 
 <div data-lang="r" markdown="1">
 
 Refer to the [R API docs](api/R/spark.fpGrowth.html) for more details.
 
 {% include_example r/ml/fpm.R %}
 </div>
 
 </div>
 
 ## PrefixSpan
 
 PrefixSpan is a sequential pattern mining algorithm described in
 [Pei et al., Mining Sequential Patterns by Pattern-Growth: The
 PrefixSpan Approach](https://doi.org/10.1109%2FTKDE.2004.77). We refer
 the reader to the referenced paper for formalizing the sequential
 pattern mining problem.
 
 `spark.ml`'s PrefixSpan implementation takes the following parameters:
 
 * `minSupport`: the minimum support required to be considered a frequent
   sequential pattern.
 * `maxPatternLength`: the maximum length of a frequent sequential
   pattern. Any frequent pattern exceeding this length will not be
   included in the results.
 * `maxLocalProjDBSize`: the maximum number of items allowed in a
   prefix-projected database before local iterative processing of the
   projected database begins. This parameter should be tuned with respect
   to the size of your executors.
 * `sequenceCol`: the name of the sequence column in dataset (default "sequence"), rows with
   nulls in this column are ignored.
 
 **Examples**
 
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
 Refer to the [Scala API docs](api/scala/org/apache/spark/ml/fpm/PrefixSpan.html) for more details.
 
 {% include_example scala/org/apache/spark/examples/ml/PrefixSpanExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
 Refer to the [Java API docs](api/java/org/apache/spark/ml/fpm/PrefixSpan.html) for more details.
 
 {% include_example java/org/apache/spark/examples/ml/JavaPrefixSpanExample.java %}
 </div>
 
 <div data-lang="python" markdown="1">
 Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.fpm.PrefixSpan) for more details.
 
 {% include_example python/ml/prefixspan_example.py %}
 </div>
 
 <div data-lang="r" markdown="1">
 
 Refer to the [R API docs](api/R/spark.prefixSpan.html) for more details.
 
 {% include_example r/ml/prefixSpan.R %}
 </div>
 
 </div>

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