Re: frequent pattern and association rule
Date: September 04, 2013 04:59AM
Hello,
I think you are referring to "frequent itemset" and "association rule".
I will explain. Association rules are usually found in two steps:
Step 1) Discover the frequent itemsets.
Input: a transaction database and a minsup threshold set by the user
Output: all set of items that appear in more than minsup transactions from the database.
Step 2) Generate association rules by using frequent itemsets
Input : the frequent itemsets found in Step1 + the minconf threshold set by the user
Output: all the association rules respecting the minsup and minconf threshold.
So basically, you can see discovering frequent itemsets as an intermediary step to generate association rules.
Now, about the algorithms names.
FPGrowth is an algorithm to discover frequent itemsets (Step1)
Apriori is an algorithm to discover frequent itemsets (Step1) and it also include another algorithm to generate association rules (Step 2).
The algorithm to generate association rules from Apriori can also be applied with FPGrowth. Therefore, it is possible to also generate association rules with FPGrowth.
Actually, FPGrowth and Apriori (Step1) generate the same result if you give them the same output. The only difference is HOW they generate the itemsets (which strategy they use and which datastructure internally). To generate the rules, they would use the same algorithm in Step2.
If you want a good introduction to itemset and association rule mining, I suggest to read this great chapter from the book "introduction to data mining":
http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
It gives a lot more information such. For example, it explains why it is useful to find itemset to generate the rules instead of trying to generate the rules directly, etc.
Hope this helps,
PHilipp
Edited 2 time(s). Last edit at 09/04/2013 05:01AM by webmasterphilfv.