Important Terms

Support:
Definition: Support measures the frequency of occurrence of an itemset in the dataset. 
It is calculated as the number of transactions containing the itemset divided by the total number of transactions in the dataset.

Purpose: Support helps identify itemsets that are frequently occurring. 
It is used to filter out infrequent itemsets, ensuring that only itemsets that meet a minimum support threshold are considered significant.

Example: If you're analyzing customer purchase data, 
the support of an itemset like {milk, bread} would be the number of transactions where both milk and bread are purchased divided by the total number of transactions.

Confidence:
Definition: Confidence measures the strength of the association between two itemsets in an association rule. 
It is calculated as the support of the combined itemset (the antecedent and consequent of the rule) divided by the support of the antecedent itemset alone.

Purpose: Confidence helps assess how likely it is that the presence of the antecedent itemset will result in the presence of the consequent itemset. 
High confidence values indicate strong associations between the itemsets.

Example: If you have an association rule {milk} -> {bread}, the confidence would be the support of {milk, bread} divided by the support of {milk}. 
It tells you the probability that customers who buy milk will also buy bread.

Lift:
Definition: Lift measures the degree of association between two itemsets in an association rule, taking into account the independence of the items. 
It is calculated as the ratio of the confidence of the rule to the support of the consequent itemset.

Purpose: Lift helps assess whether the presence of the antecedent itemset has any influence on the presence of the consequent itemset beyond what would be expected by chance. 
Lift values greater than 1 indicate a positive association, while values less than 1 suggest a negative or non-significant association.

Example: For the rule {milk} -> {bread}, the lift would be calculated as (confidence of {milk} -> {bread}) / (support of {bread}). 
A lift value greater than 1 would suggest that buying milk increases the likelihood of buying bread compared to random chance.