This is a typical use case "people who buy electric razors, also tend to
buy batteries and shaving gel along with it". The goal is to build a model
which will look through POS records and find which product categories have
higher likelihood of appearing together in given a transaction.

What would you recommend?

On Fri, Dec 5, 2014 at 7:21 AM, Sean Owen <so...@cloudera.com> wrote:

> Generally I don't think frequent-item-set algorithms are that useful.
> They're simple and not probabilistic; they don't tell you what sets
> occurred unusually frequently. Usually people ask for frequent item
> set algos when they really mean they want to compute item similarity
> or make recommendations. What's your use case?
>
> On Thu, Dec 4, 2014 at 8:23 PM, Rohit Pujari <rpuj...@hortonworks.com>
> wrote:
> > Sure, I’m looking to perform frequent item set analysis on POS data set.
> > Apriori is a classic algorithm used for such tasks. Since Apriori
> > implementation is not part of MLLib yet, (see
> > https://issues.apache.org/jira/browse/SPARK-4001) What are some other
> > options/algorithms I could use to perform a similar task? If there’s no
> > spoon to spoon substitute,  spoon to fork will suffice too.
> >
> > Hopefully this provides some clarification.
> >
> > Thanks,
> > Rohit
> >
> >
> >
> > From: Tobias Pfeiffer <t...@preferred.jp>
> > Date: Thursday, December 4, 2014 at 7:20 PM
> > To: Rohit Pujari <rpuj...@hortonworks.com>
> > Cc: "user@spark.apache.org" <user@spark.apache.org>
> > Subject: Re: Market Basket Analysis
> >
> > Hi,
> >
> > On Thu, Dec 4, 2014 at 11:58 PM, Rohit Pujari <rpuj...@hortonworks.com>
> > wrote:
> >>
> >> I'd like to do market basket analysis using spark, what're my options?
> >
> >
> > To do it or not to do it ;-)
> >
> > Seriously, could you elaborate a bit on what you want to know?
> >
> > Tobias
> >
> >
> >
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-- 
Rohit Pujari
Solutions Engineer, Hortonworks
rpuj...@hortonworks.com
716-430-6899

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