GitHub user jackylk opened a pull request:
https://github.com/apache/spark/pull/2847
[SPARK-4001][MLlib] adding apriori algorithm for frequent item set mining
in Spark
Apriori is the classic algorithm for frequent item set mining in a
transactional data set. It will be useful if Apriori algorithm is added to
MLLib in Spark. This PR add an implementation for it.
There is a point I am not sure wether it is most efficient. In order to
filter out the eligible frequent item set, currently I am using a cartesian
operation on two RDDs to calculate the degree of support of each item set, not
sure wether it is better to use broadcast variable to achieve the same.
I will add an example to use this algorithm if requires
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/jackylk/spark apriori
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/2847.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #2847
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commit da2cba7e063745aacef74ff555e7bd7c55a24f56
Author: Jacky Li <[email protected]>
Date: 2014-10-19T09:19:27Z
adding apriori algorithm for frequent item set mining in Spark
commit 889b33fdfabcc222c82e3bce619aeb6c7031fc58
Author: Jacky Li <[email protected]>
Date: 2014-10-19T09:31:04Z
modify per scalastyle check
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