Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17130#discussion_r113782786
--- Diff: docs/ml-frequent-pattern-mining.md ---
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+---
+layout: global
+title: Frequent Pattern Mining
+displayTitle: Frequent Pattern Mining
+---
+
+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](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 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
--- End diff --
Please refer to https://issues.apache.org/jira/browse/SPARK-19899
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