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https://issues.apache.org/jira/browse/SPARK-6386?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-6386.
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Resolution: Duplicate
Assocation rule algorithms are the general class, of which FP-growth and
Apriori are specific examples. As discussed in SPARK-6381, these have already
been addressed and/or previously discussed in MLlib. At the least, direct your
comments to the existing JIRAs? or let's discuss on the user@ list before going
straight to another JIRA that appears redundant.
> add association rule mining algorithm to MLLib
> ----------------------------------------------
>
> Key: SPARK-6386
> URL: https://issues.apache.org/jira/browse/SPARK-6386
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: zhangyouhua
>
> [~mengxr]
> association rule algorithm is find frequent items which are association，while
> given transition data set and minSupport and minConf. we can use FPGrowth
> algorithm mining frequent pattern item，but can not explain each other. so we
> should add association rule algorithm.
> for example：
> data set：
> A B C
> A C
> A D
> B E F
> minSupport :0.5
> minConf:0.5
> the frequent items-> support
> A ->0.75
> B ->0.5
> C ->0.5
> A C ->0.5
> use minSupport calculate minConf：
> A -> C: support {A C}/support {A} = 0.67
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