docs/mllib-frequent-pattern-mining.md - spark - Git at Google

 ---
 layout: global
 title: Frequent Pattern Mining - RDD-based API
 displayTitle: Frequent Pattern Mining - RDD-based API
 ---

 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.
 `spark.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 `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](http://dx.doi.org/10.1145/1454008.1454027).
 PFP distributes the work of growing FP-trees based on the suffixes of transactions,
 and hence more scalable than a single-machine implementation.
 We refer users to the papers for more details.

 `spark.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/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
 FP-growth algorithm.
 It takes 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/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
 that stores the frequent itemsets with their frequencies.  The following
 example illustrates how to mine frequent itemsets and association rules
 (see [Association
 Rules](mllib-frequent-pattern-mining.html#association-rules) for
 details) from `transactions`.

 Refer to the [`FPGrowth` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) for details on the API.

 {% include_example scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala %}

 </div>

 <div data-lang="java" markdown="1">

 [`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
 FP-growth algorithm.
 It takes 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.  The following
 example illustrates how to mine frequent itemsets and association rules
 (see [Association
 Rules](mllib-frequent-pattern-mining.html#association-rules) for
 details) from `transactions`.

 Refer to the [`FPGrowth` Java docs](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) for details on the API.

 {% include_example java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java %}

 </div>

 <div data-lang="python" markdown="1">

 [`FPGrowth`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) implements the
 FP-growth algorithm.
 It takes an `RDD` of transactions, where each transaction is an `List` of items of a generic type.
 Calling `FPGrowth.train` with transactions returns an
 [`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel)
 that stores the frequent itemsets with their frequencies.

 Refer to the [`FPGrowth` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) for more details on the API.

 {% include_example python/mllib/fpgrowth_example.py %}

 </div>

 </div>

 ## Association Rules

 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 [AssociationRules](api/scala/index.html#org.apache.spark.mllib.fpm.AssociationRules)
 implements a parallel rule generation algorithm for constructing rules
 that have a single item as the consequent.

 Refer to the [`AssociationRules` Scala docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API.

 {% include_example scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala %}

 </div>

 <div data-lang="java" markdown="1">
 [AssociationRules](api/java/org/apache/spark/mllib/fpm/AssociationRules.html)
 implements a parallel rule generation algorithm for constructing rules
 that have a single item as the consequent.

 Refer to the [`AssociationRules` Java docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API.

 {% include_example java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java %}

 </div>
 </div>

 ## PrefixSpan

 PrefixSpan is a sequential pattern mining algorithm described in
 [Pei et al., Mining Sequential Patterns by Pattern-Growth: The
 PrefixSpan Approach](http://dx.doi.org/10.1109%2FTKDE.2004.77). We refer
 the reader to the referenced paper for formalizing the sequential
 pattern mining problem.

 `spark.mllib`'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.

 **Examples**

 The following example illustrates PrefixSpan running on the sequences
 (using same notation as Pei et al):

 ~~~
   <(12)3>
   <1(32)(12)>
   <(12)5>
   <6>
 ~~~

 <div class="codetabs">
 <div data-lang="scala" markdown="1">

 [`PrefixSpan`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) implements the
 PrefixSpan algorithm.
 Calling `PrefixSpan.run` returns a
 [`PrefixSpanModel`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel)
 that stores the frequent sequences with their frequencies.

 Refer to the [`PrefixSpan` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) and [`PrefixSpanModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel) for details on the API.

 {% include_example scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala %}

 </div>

 <div data-lang="java" markdown="1">

 [`PrefixSpan`](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) implements the
 PrefixSpan algorithm.
 Calling `PrefixSpan.run` returns a
 [`PrefixSpanModel`](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html)
 that stores the frequent sequences with their frequencies.

 Refer to the [`PrefixSpan` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) and [`PrefixSpanModel` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html) for details on the API.

 {% include_example java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java %}

 </div>
 </div>
	---
	layout: global
	title: Frequent Pattern Mining - RDD-based API
	displayTitle: Frequent Pattern Mining - RDD-based API
	---

	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.
	`spark.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 `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](http://dx.doi.org/10.1145/1454008.1454027).
	PFP distributes the work of growing FP-trees based on the suffixes of transactions,
	and hence more scalable than a single-machine implementation.
	We refer users to the papers for more details.

	`spark.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/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
	FP-growth algorithm.
	It takes 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/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
	that stores the frequent itemsets with their frequencies. The following
	example illustrates how to mine frequent itemsets and association rules
	(see [Association
	Rules](mllib-frequent-pattern-mining.html#association-rules) for
	details) from `transactions`.

	Refer to the [`FPGrowth` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) for details on the API.

	{% include_example scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala %}

	</div>

	<div data-lang="java" markdown="1">

	[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
	FP-growth algorithm.
	It takes 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. The following
	example illustrates how to mine frequent itemsets and association rules
	(see [Association
	Rules](mllib-frequent-pattern-mining.html#association-rules) for
	details) from `transactions`.

	Refer to the [`FPGrowth` Java docs](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) for details on the API.

	{% include_example java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java %}

	</div>

	<div data-lang="python" markdown="1">

	[`FPGrowth`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) implements the
	FP-growth algorithm.
	It takes an `RDD` of transactions, where each transaction is an `List` of items of a generic type.
	Calling `FPGrowth.train` with transactions returns an
	[`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel)
	that stores the frequent itemsets with their frequencies.

	Refer to the [`FPGrowth` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) for more details on the API.

	{% include_example python/mllib/fpgrowth_example.py %}

	</div>

	</div>

	## Association Rules

	<div class="codetabs">
	<div data-lang="scala" markdown="1">
	[AssociationRules](api/scala/index.html#org.apache.spark.mllib.fpm.AssociationRules)
	implements a parallel rule generation algorithm for constructing rules
	that have a single item as the consequent.

	Refer to the [`AssociationRules` Scala docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API.

	{% include_example scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala %}

	</div>

	<div data-lang="java" markdown="1">
	[AssociationRules](api/java/org/apache/spark/mllib/fpm/AssociationRules.html)
	implements a parallel rule generation algorithm for constructing rules
	that have a single item as the consequent.

	Refer to the [`AssociationRules` Java docs](api/java/org/apache/spark/mllib/fpm/AssociationRules.html) for details on the API.

	{% include_example java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java %}

	</div>
	</div>

	## PrefixSpan

	PrefixSpan is a sequential pattern mining algorithm described in
	[Pei et al., Mining Sequential Patterns by Pattern-Growth: The
	PrefixSpan Approach](http://dx.doi.org/10.1109%2FTKDE.2004.77). We refer
	the reader to the referenced paper for formalizing the sequential
	pattern mining problem.

	`spark.mllib`'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.

	Examples

	The following example illustrates PrefixSpan running on the sequences
	(using same notation as Pei et al):

	~~~
	<(12)3>
	<1(32)(12)>
	<(12)5>
	<6>
	~~~

	<div class="codetabs">
	<div data-lang="scala" markdown="1">

	[`PrefixSpan`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) implements the
	PrefixSpan algorithm.
	Calling `PrefixSpan.run` returns a
	[`PrefixSpanModel`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel)
	that stores the frequent sequences with their frequencies.

	Refer to the [`PrefixSpan` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) and [`PrefixSpanModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel) for details on the API.

	{% include_example scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala %}

	</div>

	<div data-lang="java" markdown="1">

	[`PrefixSpan`](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) implements the
	PrefixSpan algorithm.
	Calling `PrefixSpan.run` returns a
	[`PrefixSpanModel`](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html)
	that stores the frequent sequences with their frequencies.

	Refer to the [`PrefixSpan` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) and [`PrefixSpanModel` Java docs](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html) for details on the API.

	{% include_example java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java %}

	</div>
	</div>