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"pruning redundant association rules"
Hi everyone,
I made an association rule in RapidMiner and everything es ok, but there are a lot of redundant rules, for example A=>B, B=>A. As you can see, It's the same rule.
My question is how to remove them?
I need help please. I've looked for some information about it, but I didn't found anything.
I made an association rule in RapidMiner and everything es ok, but there are a lot of redundant rules, for example A=>B, B=>A. As you can see, It's the same rule.
My question is how to remove them?
I need help please. I've looked for some information about it, but I didn't found anything.
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Answers
I would have rather said that A=>B and B=>A are two different rules...
Anyways can you share your XML process and your dataset(s) ?
Regards,
Lionel
Can you explain me why A=>B and B=>A are different rules? For example if I have two rules, one says "IF buy Milk Then buy Bread" and the other says "IF buy Bread Then buy Milk". Aren't them redundant rules?
Sorry, How can I share my xml process? I am new using rapidminer.
Regards,
Andre
I think it is 2 different rules, because it corresponds to different customer behaviours.
If a client is buying bread, you will propose him some milk
for an other client who is buying some milk, you will propose him some bread
==> it is two different customer behaviours and thus you have to apply a different recommendation (rule) in each case.
Anyway, I managed without your process and your data. You can find a process performing what you want to do (to adapt to your own data ...) :
Open your process in RapidMiner and open the XML panel..
Copy the XML code from there and paste it somewhere else, for example into a forum post here on the community portal. By the way, if you post your XML here, please use the code environment which you get by clicking on the </> icon in the toolbar of the post.
In order to import such an XML description of your process, e.g. to use a process someone else has posted here in the forum, please follow the following steps:
I hope it helps,
Regards,
Lionel
Lindon Ventures
Data Science Consulting from Certified RapidMiner Experts
the customer is buying coca cola since he buys rum, this a conditional probability. It means when he buys rum he will buy coca cola to mix his drink, but it's not the same tha he will rum since he buys coca cola. Buy coca cola it will not force him buy rum.
the first option it will have a higher correlation, but the second one no.
you can confirm with the confidence and with the lift.