Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/17130#discussion_r108441341
--- 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 suffices of
transactions,
--- End diff --
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