what is the difference between Association rule mining & frequent itemset mining

Question

i am new to data mining and confuse about Association rules and frequent item mining. for me i think both are same but i need views from experts on this forum

My questio

Answer 1

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 conf(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.