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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