What is the difference between Association rule mining & frequent itemset mining?

C", meaning that C tends to occur when A and B occur. An itemset is just a collection such as "A,B,C", and it is frequent if its items tend to co-occur The usual way to look for association rules is to find all frequent itemsets and then postprocess them into rules.

C", meaning that C tends to occur when A and B occur. An itemset is just a collection such as "A,B,C", and it is frequent if its items tend to co-occur. The usual way to look for association rules is to find all frequent itemsets and then postprocess them into rules.

The input of frequent itemset mining is : a transaction database a minimum support threshold minsup The output is : the set of all itemsets appearing in at least minsup transactions. An itemset is just a set of items that is unordered. The input of assocition rule mining is : a transaction database a minimum support threshold minsup a minimum confidence threshold minconf The output is : the set of all valid association rule.An association rule X-->Y is a relationship between two itemsets X and Y such that X and Y are disjoint and are not empty.

A valid rule is a rule having a support higher or equals to minsup and a confidence higher or equal to minconf. The support is defined as sup(x-->Y) = sup (X U Y) / (number of transactions). The confidence is defined as sup(x-->Y) = sup (X U Y) / sup (X).

Now the relationship between itemset and association rule mining is that it is very efficient to use the frequent itemset to generate rules (see the paper by Agrawal 1993) for more details about this idea. So association rule mining will be broken down into two steps: - mining frequent itemsets - generating all valid association rules by using the frequent itemsets.

Frequent itemset mining is a step of Association rules mining. After applying Frequent itemset mining algorithm like Apriori, FPGrowth on data, you will get frequent itemsets. From these discovered frequent itemsets, you will generate association rules(Usually done by subset generation).

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