Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17130#discussion_r108600468
--- Diff: docs/ml-frequent-pattern-mining.md ---
@@ -0,0 +1,75 @@
+---
+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 suffices of
transactions,
+and hence 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. 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
+ partition number 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`: The transform method examines the input items in `itemsCol`
against all the association rules and
+ summarize the consequents as prediction. The prediction column has the
same data type as the
--- End diff --
Thanks for the suggestion. I do wish to have a better illustration here.
But the two containing in your version make it not that straightforward, and
actually it should be items in `itemsCol ` contains the antecedents for
association rules.
I extend it to a longer version,
For each record 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.
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