problem statement:

1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values
2) Change the minimum length in apriori algorithm
3) Visulize the obtained rules using different plots 

**NOTE
Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction.

Important terms:
Support Count(\sigma) – Frequency of occurrence of a itemset.

Frequent Itemset – An itemset whose support is greater than or equal to minsup threshold.

Support(s) –
The number of transactions that include items in the {X} and {Y} parts of the rule as a percentage of the total number of transaction.It is a measure of how frequently the collection of items occur together as a percentage of all transactions.

Confidence(c) –
It is the ratio of the no of transactions that includes all items in {B} as well as the no of transactions that includes all items in {A} to the no of transactions that includes all items in {A}.

Lift(l) –
The lift of the rule X=>Y is the confidence of the rule divided by the expected confidence, assuming that the itemsets X and Y are independent of each other.The expected confidence is the confidence divided by the frequency of {Y}.