Market basket analysis scrutinizes the products customers tend to buy together, and uses the information to decide which products should be cross-sold or promoted together. The term arises from the shopping carts supermarket shoppers fill up during a shopping trip.
We can use MBA to extract interesting association between products from the data. Hence its output consists of a series of product association rules: for example, if customers buy product A they also tend to buy product B. We will follow the three most popular criteria evaluating the quality or the strength of an association rule
- Support -> P(AB)
- Confidence -> P(B|A)
- Lift -> P(B|A)/P(B)
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Support is the percentage of transactions containing a particular combination of items relative to the total number of transactions in the database. The support for the combination A and B would be, P(AB) or P(A) for Individual A
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Confidence measures how much the consequent (item) is dependent on the antecedent (item). In other words, confidence is the conditional probability of the consequent given the antecedent, P(B|A) where P(B|A) = P(AB)/P(A)
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Lift (also called improvement or impact) is a measure to overcome the problems with support and confidence. Lift is said to measure the difference — measured in ratio — between the confidence of a rule and the expected confidence. Consider an association rule “if A then B.” The lift for the rule is defined as P(B|A)/P(B) or P(AB)/[P(A)P(B)].
Each criterion has its advantages and disadvantages but in general we would like association rules that have high confidence, high support, and high lift.