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Frequent Pattern Mining - RDD-based API |
Frequent Pattern Mining - RDD-based API |
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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
for more information.
spark.mllib provides a parallel implementation of FP-growth,
a popular algorithm to mining frequent itemsets.
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 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.
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
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
that stores the frequent itemsets with their frequencies. The following
example illustrates how to mine frequent itemsets and association rules
(see Association
Rules for
details) from transactions.
Refer to the FPGrowth Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/mllib/SimpleFPGrowth.scala %}
FPGrowth 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
that stores the frequent itemsets with their frequencies. The following
example illustrates how to mine frequent itemsets and association rules
(see Association
Rules for
details) from transactions.
Refer to the FPGrowth Java docs for details on the API.
{% include_example java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java %}
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
that stores the frequent itemsets with their frequencies.
Refer to the FPGrowth Python docs for more details on the API.
{% include_example python/mllib/fpgrowth_example.py %}
Refer to the AssociationRules Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/mllib/AssociationRulesExample.scala %}
Refer to the AssociationRules Java docs for details on the API.
{% include_example java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java %}
PrefixSpan is a sequential pattern mining algorithm described in Pei et al., Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. 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>
PrefixSpan implements the
PrefixSpan algorithm.
Calling PrefixSpan.run returns a
PrefixSpanModel
that stores the frequent sequences with their frequencies.
Refer to the PrefixSpan Scala docs and PrefixSpanModel Scala docs for details on the API.
{% include_example scala/org/apache/spark/examples/mllib/PrefixSpanExample.scala %}
PrefixSpan implements the
PrefixSpan algorithm.
Calling PrefixSpan.run returns a
PrefixSpanModel
that stores the frequent sequences with their frequencies.
Refer to the PrefixSpan Java docs and PrefixSpanModel Java docs for details on the API.
{% include_example java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java %}