Home Knowledge Management Frequent Itemset in Data set (Association Rule Mining)

# Frequent Itemset in Data set (Association Rule Mining)

Association Mining searches for frequent items in the data-set. In frequent mining usually the interesting associations and correlations between item sets in transactional and relational databases are found.

In short, Frequent Mining shows which items appear together in a transaction or relation.

Need of Association Mining:
Frequent mining is the generation of association rules from a Transactional Dataset. If there are 2 items X and Y purchased frequently then it’s good to put them together in stores or provide some discount offer on one item on purchase of other items. This can really increase sales.

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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.

Now, Let discuss about some important definitions

### What is Support?

It is one of the measures of interestingness. This tells about the usefulness and certainty of rules. 5% Support means a total of 5% of transactions in the database follow the rule.

`Support(A -> B) = Support_count(A ∪ B)`

### What is Confidence?

A confidence of 60% means that 60% of the customers who purchased milk and bread also bought butter.

`Confidence(A -> B) = Support_count(A ∪ B) / Support_count(A)`

If a rule satisfies both minimum support and minimum confidence, it is a strong rule.

Support_count(X) : Number of transactions in which X appears. If X is A union B then it is the number of transactions in which A and B both are present.

### What is Maximal Itemset ?

An itemset is maximal frequent if none of its supersets are frequent.

### What is Closed Itemset?

An itemset is closed if none of its immediate supersets have same support count same as Itemset.

### What is K-Itemset?

Itemset which contains K items is a K-itemset. So it can be said that an itemset is frequent if the corresponding support count is greater than minimum support count.

Example On finding Frequent Itemsets –
Consider the given dataset with given transactions.

• Lets say minimum support count is 3
• Relation hold is maximal frequent => closed => frequent

1-frequent:
{A} = 3; // not closed due to {A, C} and not maximal
{B} = 4; // not closed due to {B, D} and no maximal
{C} = 4; // not closed due to {C, D} not maximal
{D} = 5; // closed item-set since not immediate super-set has same count. Not maximal

2-frequent:
{A, B} = 2 // not frequent because support count < minimum support count so ignore
{A, C} = 3 // not closed due to {A, C, D}
{A, D} = 3 // not closed due to {A, C, D}
{B, C} = 3 // not closed due to {B, C, D}
{B, D} = 4 // closed but not maximal due to {B, C, D}
{C, D} = 4 // closed but not maximal due to {B, C, D}

3-frequent:
{A, B, C} = 2 // ignore not frequent because support count < minimum support count
{A, B, D} = 2 // ignore not frequent because support count < minimum support count
{A, C, D} = 3 // maximal frequent
{B, C, D} = 3 // maximal frequent

4-frequent:
{A, B, C, D} = 2 //ignore not frequent
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