Working Principle of Apriori Algorithm

The video tutorial explains market basket analysis using association rules to identify products frequently bought together. It introduces the Apriori algorithm and key metrics like support, confidence, and lift. The tutorial guides through calculating these metrics, generating frequent item sets, and forming association rules to enhance marketing strategies.

What is the primary goal of Market Basket Analysis?

• To increase the number of products in a store

• To find items frequently purchased together

• To reduce the cost of products

• To improve customer service

In association rules, what is the term for the item set on the left-hand side of a rule?

• Consequent

• Support

• Antecedent

• Confidence

Which metric measures how often an item set appears in the transaction data?

• Lift

• Support

• Antecedent

• Confidence

What is the minimum support threshold used for in the Apriori algorithm?

• To determine the most expensive items

• To filter out infrequent item sets

• To calculate the total sales

• To identify the best-selling product

How is confidence calculated in association rules?

• By dividing the transactions containing both A and B by the transactions containing A

• By subtracting the support of B from the support of A

• By multiplying the support of A and B

• By dividing the support of A by the support of B

What does a lift value greater than one indicate?

• Items A and B are never bought together

• Items A and B are equally likely to be bought separately

• Items A and B are more likely to be bought together

• Items A and B are less likely to be bought together

What is the final step in the process of generating association rules?

• Setting a minimum value for support

• Ordering rules by descending order of lift

• Identifying the most expensive item

• Calculating the total number of transactions