Question 11: You are using the Apriori algorithm to determine the likelihood that a person who owns a home
has a good credit score. You have determined that the confidence for the rules used in the algorithm is > 75%. You calculate lift = 1.011 for the rule, "People with good credit are homeowners". What can you determine from the lift calculation?

1.  Support for the association is low

2.  Leverage of the rules is low

3. The rule is coincidental

4. The rule is true

Correct Answer : 3 Exp: Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.
The whole point of the algorithm (and data mining, in general) is to extract useful information from large amounts of data. For example, the information that a customer who purchases a keyboard also tends to buy a mouse at the same time is acquired from the association rule below:

Support: The percentage of task-relevant data transactions for which the pattern is true.

Support (Keyboard -> Mouse) = No. of Transactions containing both Keyboards and Mouse/No. of total transactions

Confidence: The measure of certainty or trustworthiness associated with each discovered pattern.

Confidence (Keyboard -> Mouse) = No. of Transactions containing both Keyboards and Mouse/No. of transactions containing (Keyboard)

The algorithm aims to find the rules which satisfy both a minimum support threshold and a minimum confidence threshold (Strong Rules).