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From BenFradet <>
Subject [GitHub] spark pull request #17130: [SPARK-19791] [ML] Add doc and example for fpgrow...
Date Tue, 28 Mar 2017 15:14:33 GMT
Github user BenFradet commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -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
    +first steps to analyze a large-scale dataset, which has been an active research topic
    +data mining for years.
    +We refer users to Wikipedia's [association rule learning](
    +for more information.
    +**Table of Contents**
    +* This will become a table of contents (this text will be scraped).
    +## FP-Growth
    +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 suffices of transactions,
    +and hence more scalable than a single-machine implementation.
    +We refer users to the papers for more details.
    +``'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.
    --- End diff --
    also, there are two commas after itemsets

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