spark-reviews mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From BenFradet <...@git.apache.org>
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:

    https://github.com/apache/spark/pull/17130#discussion_r108450311
  
    --- 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.
    --- End diff --
    
    also, there are two commas after itemsets


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org


Mime
View raw message