Hi,
Is there a good way to remove all the subsets of patterns from the output
given by FP Growth?
For example if both the patterns pass the confidence and support thresholds:
[Attribute1 = A, Attribute2 = B] -> [Output=C]
[Attribute1 = A] -> [Output=C]
I want to choose only [Attribute1 = A] ->
Hi Shivin,
I'm interested in collaborating with you on this project.
I have been using pyspark for a while now and quite familiar with it.
Do you have any plan on how to proceed?
Thanks,
Aditya
On Sat, 27 Jun, 2020, 2:58 pm Shivin Srivastava,
wrote:
> Hi All,
>
> I have recently been explori
d. Not sure how to optimize that.
>
> On Thu, May 7, 2020, 1:12 PM Aditya Addepalli wrote:
>
>> Hi Sean,
>>
>> 1.
>> I was thinking that by specifying the consequent we can (somehow?) skip
>> the confidence calculation for all the other consequents.
>>
mputing all that support; how
> would you optimize it even if you knew the consequent you cared about?
> maybe there's a way, sure, I don't know the code well but it wasn't
> obvious at a glance how to take advantage of it.
>
> I can see how limiting the rule size could
Hi,
I understand that this is not a priority with everything going on, but if
you think generating rules for only a single consequent adds value, I would
like to contribute.
Thanks & Regards,
Aditya
On Sat, May 2, 2020 at 9:34 PM Aditya Addepalli wrote:
> Hi Sean,
>
> I un
n the data.
Sometimes this can be 1e-4 or 1e-5, so my minSupport has to be less than
that to capture the rules for that consequent.
Thanks for your reply. Let me know what you think.
Regards.
Aditya Addepalli
On Sat, 2 May, 2020, 9:13 pm Sean Owen, wrote:
> You could just filter the i
I am not sure
that is feasible.
I am willing to work on these suggestions, if someone thinks they are
feasible. Thanks to the dev team for all the hard work!
Regards,
Aditya Addepalli
