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From 戴清灏 <rogerda...@gmail.com>
Subject Re: Frequent itemset mining
Date Fri, 02 Dec 2011 05:54:56 GMT
For a sequential implementation, fpgrowth.java might be the first.
For a parallel implementation, pfpgrowth.java might be.
there are 5 steps at total and 4 out of them are mapreduce.

Sent from my mobile phone
在 2011-12-2 下午12:48,"Dave Fry" <dfry@upstreamsoftware.com>写道:

> That would be fantastic, thank you!
>
> In the meantime, can you direct me to where in the source I should start
> looking?  (ie, which class would be the entry point I'm looking for?)
>
> 2011/12/1 戴清灏 <rogerdai16@gmail.com>
>
> > There is actually a lack of the doc for the frequent pattern mining
> usage.
> > Actually, you are not the first one who claims the need of it.
> > I will be pleased to write one for that usage since I've read almost the
> > source code of it.
> >
> > 在 2011年12月2日星期五,Dave Fry 写道:
> >
> > > Hi!  I apologize for the newbie question, I'm just getting started with
> > > Mahout.
> > >
> > > On the "Overview" page on Mahout's website:
> > > https://cwiki.apache.org/confluence/display/MAHOUT/Overview
> > >
> > > It mentions this as the four primary targeted use cases for Mahout:
> > > 1) Recommendation mining takes users' behavior and from that tries to
> > find
> > > items users might like.
> > > 2) Clustering takes e.g. text documents and groups them into groups of
> > > topically related documents.
> > > 3) Classification learns from exisiting categorized documents what
> > > documents of a specific category look like and is able to assign
> > unlabelled
> > > documents to the (hopefully) correct category.
> > > 4) Frequent itemset mining takes a set of item groups (terms in a query
> > > session, shopping cart content) and identifies, which individual items
> > > usually appear together.
> > >
> > > But, based on the Mahout documentation that I've read through, I can't
> > seem
> > > to find a clear mapping from that use case description to where in the
> > > Mahout distribution I should be looking.  I've found several leads for
> > use
> > > case #1, but #4 seems to be a bit of a mystery (and searches for
> > "frequent
> > > itemset mining" don't seem to lead me to where I need to go.)
> > >
> > > Basically, I'm looking to the answer to the question "Which items
> appear
> > > most often with item X in browse histories and shopping carts?".  (As
> > > opposed to "Based on what I know about your preferences, here are the
> > items
> > > that I predict you would be most likely to browse/add to your cart".)
> > >
> > > Any help is appreciated!
> > > Thanks,
> > > Dave
> > >
> >
> >
> > --
> > Regards,
> > Q
> >
>

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