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

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).

Answer 2

By using Association rule mining we will get the frequently itemsets that present in the given dataset. it also provide different types of algorithms for mining the frequent itemsets but it is done in different way that means either horizontal or vertical format. Apriori algorithm follow the horizontal format for mining the frequent itemsets and eclat algorithm follow the vertical format for mining the frequent datasets.

Answer 3

An association rule is something like "A,B → 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.

Answer 4

Association Rule mining:

Association rule mining is used to find the patterns in data.it finds the features which occur together and correlated.

• Example:

For example, people who buy diapers are likely to buy baby powder. Or we can rephrase the statement by saying: If (people buy diaper), then (they buy baby powder). Note the if, then rule. This does not necessarily mean that if people buy baby powder, they buy diaper. In General, we can say that if condition A tends to B it does not necessarily mean that B tends to A.

Frequent item set mining:

Frequent item set mining is used to find the common item sets in data. it can generate association rules from the given transactional datasets.

• Example:

If there are 2 items X and Y purchased frequently then its good to put them together in stores or provide some discount offer on one item on purchase of other item. This can really increase the sales. For example it is likely to find that if a customer buys Milk and bread he/she also buys Butter. So the association rule is [‘milk]^[‘bread’]=>[‘butter’]. So seller can suggest the customer to buy butter if he/she buys Milk and Bread.

Answer 5

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.

Answer 6

Frequent itemset mining is the first step of Association rule mining. Once you have generated all the frequent itemsets, you proceed by iterating over them, one by one, enumerating through all the possible association rules, calculate their confidence, finally, if the confidence is > minConfidence, you output that rule.